Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
In today’s fast-paced business world, effective visual communication is more important than ever. This is where Apple Vision Pro comes in, serving as the ultimate tool for enhancing visual communication in the workplace. With its innovative software solution, it revolutionizes the way businesses communicate by providing new and improved methods for creating, editing, and sharing visual content. Apple Vision Pro boasts a user-friendly interface and advanced features that have made it an indispensable asset for businesses seeking to streamline their visual communication processes and boost productivity. The software empowers businesses to generate professional-looking visuals that are easily shareable and foster collaboration, making it an invaluable tool for staying ahead in today’s competitive landscape.
Apple Vision Pro sets itself apart with cutting-edge features such as augmented reality and machine learning, enabling users to create immersive and informative content. Through this tool, businesses can effortlessly produce and distribute videos, presentations, and other visual materials, making it a must-have for business owners, marketers, and creative professionals alike.
The impact of Apple Vision Pro is transformative, revolutionizing how businesses communicate visually. With its advanced capabilities and intuitive interface, it empowers users to craft visually stunning content that not only captivates but also educates. The tool offers a diverse range of visual elements like charts, graphs, images, and videos, enabling the effective conveyance of complex information to audiences. Furthermore, Apple Vision Pro facilitates real-time collaboration, making it easier for teams to work together, generate content collectively, and share ideas seamlessly. These capabilities enable businesses to enhance their visual communication efforts and create more impactful content, ultimately driving them towards achieving their goals with greater efficiency and effectiveness.
Some of the potential applications of Apple Vision Pro:
Gaming: Vision Pro could be used for gaming by providing a more immersive experience.
Productivity: Vision Pro could be used for productivity applications by providing a more natural way to interact with computers.
Creative applications: Vision Pro could be used for creative applications by providing a more immersive way to create and edit content.
Education: Vision Pro could be used for education by providing a more immersive way to learn.
Training: Vision Pro could be used for training by providing a more immersive way to learn new skills.
Remote collaboration: Vision Pro could be used for remote collaboration by providing a more immersive way to work with others.
The Use of ChatGPT in the World of BioInformatics and Cancer Research and Development of BioGPT by MIT
Curator: Stephen J. Williams, Ph.D.
Chatbots are being used in multiple interdisciplanary areas in research and medicine and so it was a natural progression to incorporate artificial intelligence (AI), natural language processing (NLP), and chatbot technology like ChatGPT into bioinformatic analysis.
“In domains like informatics, management and marketing, media and communication science, languages and philosophy, psychology and sociology, engineering, design, and human-computer interaction, the fast expanding body of chatbot study is clearly interdisciplinary.
The field of bioinformatics is a natural fit for incorporting this technology. The curated information below shows some use examples of this technology in bioinformatics related to cancer research.
How ChatGPT can be used in bioinformatics research?
ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model that was designed to generate human-like text in a conversational setting. It is not directly related to bioinformatics, which is the field of study that deals with the storage, retrieval, and analysis of biological data, particularly in the context of genetics and genomics. However, ChatGPT could potentially be used in a bioinformatics context as a tool for generating natural language descriptions of genetic or genomic data, or for generating responses to queries about such data.
For example, a researcher could use ChatGPT to generate descriptions of genetic variants or gene expression patterns based on input data. This could be useful for generating summaries of results for reports or papers, or for generating explanations of complex concepts for non-expert audiences. ChatGPT could also be used to generate responses to queries about genetic or genomic data, such as “What is the function of gene X?” or “What are the potential consequences of variant Y?”
It’s worth noting that ChatGPT is just one of many tools and techniques that can be used in bioinformatics, and it is unlikely to be the most suitable or effective option in every situation. There are many specialized tools and resources available for working with biological data, and it is often necessary to use a combination of these tools to fully analyze and understand the data.
Generating descriptions of genetic or genomic data: ChatGPT could be used to generate natural language descriptions of genetic or genomic data based on input data. For example, suppose a researcher has a dataset containing information about gene expression levels in different tissues. The researcher could use ChatGPT to generate a description of the data, such as:
“Gene X is highly expressed in the liver and kidney, with moderate expression in the brain and heart. Gene Y, on the other hand, shows low expression in all tissues except for the lung, where it is highly expressed.”
Thereby ChatGPT, at its simplest level, could be used to ask general questions like “What is the function of gene product X?” and a ChatGPT could give a reasonable response without the scientist having to browse through even highly curated databases lie GeneCards or UniProt or GenBank. Or even “What are potential interactors of Gene X, validated by yeast two hybrid?” without even going to the curated InterActome databases or using expensive software like Genie.
Summarizing results: ChatGPT could be used to generate summaries of results from genetic or genomic studies. For example, a researcher might use ChatGPT to generate a summary of a study that found a association between a particular genetic variant and a particular disease. The summary might look something like this:
“Our study found that individuals with the variant form of gene X are more likely to develop disease Y. Further analysis revealed that this variant is associated with changes in gene expression that may contribute to the development of the disease.”
It’s worth noting that ChatGPT is just one tool that could potentially be used in these types of applications, and it is likely to be most effective when used in combination with other bioinformatics tools and resources. For example, a researcher might use ChatGPT to generate a summary of results, but would also need to use other tools to analyze the data and confirm the findings.
ChatGPT is a variant of the GPT (Generative Pre-training Transformer) language model that is designed for open-domain conversation. It is not specifically designed for generating descriptions of genetic variants or gene expression patterns, but it can potentially be used for this purpose if you provide it with a sufficient amount of relevant training data and fine-tune it appropriately.
To use ChatGPT to generate descriptions of genetic variants or gene expression patterns, you would first need to obtain a large dataset of examples of descriptions of genetic variants or gene expression patterns. You could use this dataset to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants or gene expression patterns.
Here’s an example of how you might use ChatGPT to generate a description of a genetic variant:
First, you would need to pre-process your dataset of descriptions of genetic variants to prepare it for use with ChatGPT. This might involve splitting the descriptions into individual sentences or phrases, and encoding them using a suitable natural language processing (NLP) library or tool.
Next, you would need to fine-tune the ChatGPT model on the task of generating descriptions of genetic variants. This could involve using a tool like Hugging Face’s Transformers library to load the ChatGPT model and your pre-processed dataset, and then training the model on the task of generating descriptions of genetic variants using an appropriate optimization algorithm.
Once the model has been fine-tuned, you can use it to generate descriptions of genetic variants by providing it with a prompt or seed text and asking it to generate a response. For example, you might provide the model with the prompt “Generate a description of a genetic variant associated with increased risk of breast cancer,” and ask it to generate a response. The model should then generate a description of a genetic variant that is associated with increased risk of breast cancer.
It’s worth noting that generating high-quality descriptions of genetic variants or gene expression patterns is a challenging task, and it may be difficult to achieve good results using a language model like ChatGPT without a large amount of relevant training data and careful fine-tuning.
To train a language model like chatGPT to extract information about specific genes or diseases from research papers, you would need to follow these steps:
Gather a large dataset of research papers that contain information about the specific genes or diseases you are interested in. This dataset should be diverse and representative of the types of papers you want the model to be able to extract information from.
Preprocess the text data in the research papers by tokenizing the text and creating a vocabulary. You may also want to consider lemmatizing or stemming the text to reduce the dimensionality of the dataset.
Train the language model on the preprocessed text data. You may want to fine-tune a pre-trained model such as chatGPT on your specific dataset, or you can train a new model from scratch.
ChatGPT could also be useful for sequence analysis
A few examples of sequence analysis a ChatGPT could be useful include:
Protein structure
Identifying functional regions of a protein
Predicting protein-protein interactions
Identifying protein homologs
Generating Protein alignments
All this could be done without having access to UNIX servers or proprietary software or knowing GCG coding
ChatGPT in biomedical research
There are several potential ways that ChatGPT or other natural language processing (NLP) models could be applied in biomedical research:
Text summarization: ChatGPT or other NLP models could be used to summarize large amounts of text, such as research papers or clinical notes, in order to extract key information and insights more quickly.
Data extraction: ChatGPT or other NLP models could be used to extract structured data from unstructured text sources, such as research papers or clinical notes. For example, the model could be trained to extract information about specific genes or diseases from research papers, and then used to create a database of this information for further analysis.
Literature review: ChatGPT or other NLP models could be used to assist with literature review tasks, such as identifying relevant papers, extracting key information from papers, or summarizing the main findings of a group of papers.
Predictive modeling: ChatGPT or other NLP models could be used to build predictive models based on large amounts of text data, such as electronic health records or research papers. For example, the model could be trained to predict the likelihood of a patient developing a particular disease based on their medical history and other factors.
It’s worth noting that while NLP models like ChatGPT have the potential to be useful tools in biomedical research, they are only as good as the data they are trained on, and it is important to carefully evaluate the quality and reliability of any results generated by these models.
ChatGPT in text mining of biomedical data
ChatGPT could potentially be used for text mining in the biomedical field in a number of ways. Here are a few examples:
Extracting information from scientific papers: ChatGPT could be trained on a large dataset of scientific papers in the biomedical field, and then used to extract specific pieces of information from these papers, such as the names of compounds, their structures, and their potential uses.
Generating summaries of scientific papers: ChatGPT could be used to generate concise summaries of scientific papers in the biomedical field, highlighting the main findings and implications of the research.
Identifying trends and patterns in scientific literature: ChatGPT could be used to analyze large datasets of scientific papers in the biomedical field and identify trends and patterns in the data, such as emerging areas of research or common themes among different papers.
Generating questions for further research: ChatGPT could be used to suggest questions for further research in the biomedical field based on existing scientific literature, by identifying gaps in current knowledge or areas where further investigation is needed.
Generating hypotheses for scientific experiments: ChatGPT could be used to generate hypotheses for scientific experiments in the biomedical field based on existing scientific literature and data, by identifying potential relationships or associations that could be tested in future research.
PLEASE WATCH VIDEO
In this video, a bioinformatician describes the ways he uses ChatGPT to increase his productivity in writing bioinformatic code and conducting bioinformatic analyses.
He describes a series of uses of ChatGPT in his day to day work as a bioinformatian:
Using ChatGPT as a search engine: He finds more useful and relevant search results than a standard Google or Yahoo search. This saves time as one does not have to pour through multiple pages to find information. However, a caveat is ChatGPT does NOT return sources, as highlighted in previous postings on this page. This feature of ChatGPT is probably why Microsoft bought OpenAI in order to incorporate ChatGPT in their Bing search engine, as well as Office Suite programs
ChatGPT to help with coding projects: Bioinformaticians will spend multiple hours searching for and altering open access available code in order to run certain function like determining the G/C content of DNA (although there are many UNIX based code that has already been established for these purposes). One can use ChatGPT to find such a code and then assist in debugging that code for any flaws
ChatGPT to document and add coding comments: When writing code it is useful to add comments periodically to assist other users to determine how the code works and also how the program flow works as well, including returned variables.
One of the comments was interesting and directed one to use BIOGPT instead of ChatGPT
0:54 oh dear. You cannot use chatgpt like that in Bioinformatics as it is rn without double checking the info from it. You should be using biogpt instead for paper summarisation. ChatGPT goes for human-like responses over precise information recal. It is quite good for debugging though and automating boring awkward scripts
The BioGPT model was proposed in BioGPT: generative pre-trained transformer for biomedical text generation and mining by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical text generation and mining. BioGPT follows the Transformer language model backbone, and is pre-trained on 15M PubMed abstracts from scratch.
The abstract from the paper is the following:
Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.
Tips:
BioGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left.
BioGPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows BioGPT to generate syntactically coherent text as it can be observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) as input, which is the previously computed key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the BioGptForCausalLM.forward() method for more information on its usage.
This model was contributed by kamalkraj. The original code can be found here.
BioGPT is MIT-licensed. The license applies to the pre-trained models as well.
Contributing
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
As of right now this does not seem Open Access, however a sign up is required!
We provide our pre-trained BioGPT model checkpoints along with fine-tuned checkpoints for downstream tasks, available both through URL download as well as through the Hugging Face 🤗 Hub.
Luo R, Sun L, Xia Y, Qin T, Zhang S, Poon H, Liu TY.Brief Bioinform. 2022 Nov 19;23(6):bbac409. doi: 10.1093/bib/bbac409.PMID: 36156661
We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. …Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fl …
Here they show the advantages of BioGPT over BioBERT and ChatGPT in an example of text extraction and question answer
Table 7. Example of generated text
Input Model Text
Bicalutamide GPT-2 Bicalutamide, a synthetic bicalutamide derivative, was found to increase the cellular proliferation
of C. elegans cells. This effect was not observed with the other synthetic bicalutamide derivatives. This is in line with our previous work which showed that bicalutamide increases the proliferation of C. elegans cells but not in the presence of synthetic bicalutamide.
BioGPT Bicalutamide (Casodex) is an androgen receptor (AR) antagonist approved for the treatment of metastatic castration-resistant prostate cancer (mCRPC) in patients who have progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of early-stage prostate cancer in men who have not progressed on or are ineligible for docetaxel chemotherapy, as well as for the treatment of metastatic castration-sensitive prostate cancer (mCSPC) in men who have not progressed on or are ineligible for docetaxel chemotherapy.
Janus kinase 3 (JAK-3) GPT-2 Janus kinase 3 (JAK-3) mediates the conversion of glucose to glycogen in skeletal muscle. The
increase in glucose uptake by skeletal muscle is believed to be associated with a reduction in muscle protein breakdown and with the release of ketone bodies, which contribute to the metabolism of glucose.
BioGPT Janus kinase 3 (JAK-3) is a member of the Janus kinase (JAK) family of non-receptor tyrosine kinases and plays an important role in the regulation of cell proliferation, differentiation, survival, migration and angiogenesis.
Apricitabine GPT-2 Apricitabine, duloxetine and citalopram) were investigated in patients with a history of
treatment-resistant depression (n = 10). We found that treatment with the three drugs significantly decreased depressive symptoms, but not depressive symptoms over the next 12 weeks. We conclude that antidepressant drugs that reduce depressive symptoms and reduce depressive symptoms over time may be beneficial in patients with recurrent depression.
BioGPT Apricitabine is an oral prodrug of 5-aza-2 ’-deoxycytidine (5-aza-CdR), a DNA methyltransferase (DNMT) inhibitor, which has been approved by the US Food and Drug Administration (FDA) for the treatment of myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) in combination with low-dose cytarabine (Ara-C) and granulocyte colony-stimulating factor (G-CSF) for patients with intermediate-2 or high-risk MDS or AML.
Xylazine GPT-2 Xylazine, the ‘active ingredient’ in ‘bath salts’ (see above) has been linked to numerous deaths,
including those of former University of California, Berkeley, professor Andrew Anglin, who posted
a video of himself having a bath filled with it.
BioGPT Xylazine is an alpha 2-adrenoceptor agonist which has been used as a sedative and analgesic in veterinary medicine for many years, but its effects on the cardiovascular system have not been extensively studied in the dog, and its effects on the central nervous system (CNS) have not been well characterized in the dog, despite the fact that xylazine has been widely used as a sedative and analgesic in veterinary medicine for more than 30 years.
Psoralen GPT-2 Psoralen, the city of the spiritualist, and had been given to him by God, who wished to show him
the way of salvation, and to draw him back to the true life of Christ, and to bring him into communion with his fellow-man.
BioGPT Psoralen photochemotherapy (PUVA) is a well-established treatment for psoriasis, but its use is limited by the risk of skin cancer, particularly squamous cell carcinoma (SCC) of the head and neck (H & N), which is the most common site of skin cancer in the United States (US)
CP-673451 GPT-2 CP-673451) and 4-5-6-3-1-2-3-4-6-7-8-9-10-11-12-13-14-15-16-17-18-19-20-21-22-23-24-25-26-27-
BioGPT CP-673451 is a potent, selective, and orally active inhibitor of human neutrophil elastase (HNE) and human cathepsin G (CatG) with in vitro and in vivo anti-inflammatory activity in a variety of animal models of inflammation and in a model of acute lung injury (ALI) in the rat induced by intratracheal instillation of lipopolysaccharide (LPS) and tumor necrosis factor-alpha (TNF-alpha), a model of acute lung injury (ALI) in which neutrophils play an important role.
BIIB-021 GPT-2 BIIB-021A-2-1.pdf. Wright, C. (2002). ‘The War on Terror: A Legal Theory of Terrorist Attacks,’ in
L.E.R. Wright, S.R. Warrick and J.H. Rothman (Eds.), The War on Terror: Essays in Honor of Michael T. Klare (New York: Oxford University Press), 143-57.
BioGPT BIIB-021 is a novel, orally active, non-peptide bradykinin B2 receptor antagonist with potent and long-lasting anti-inflammatory activity in animal models of acute and chronic inflammation and in a rat model of adjuvant-induced arthritis (AIA), an animal model of rheumatoid arthritis (RA) and in a rat model of collagen-induced arthritis (CIA), an animal model of collagen-induced arthritis (CIA), in which arthritis is induced by immunization with bovine type II collagen (CII).
Huang L, Lin J, Li X, Song L, Zheng Z, Wong KC.Brief Bioinform. 2022 Jan 17;23(1):bbab451. doi: 10.1093/bib/bbab451.PMID: 34791012
The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pretrained on biomedical corpus. In particular, we propose the multihead self-attention mechanism and packed BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pretrained language model BioGPT-2 where the generation sentences are selected based on filtering rules.
Results: We evaluated the classification part on ‘DDIs 2013’ dataset and ‘DTIs’ dataset, achieving the F1 scores of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the existing growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset demonstrated the potential of EGFI to identify novel drug relationships.
Jin Q, Yang Y, Chen Q, Lu Z.ArXiv. 2023 May 16:arXiv:2304.09667v3. Preprint.PMID: 37131884 Free PMC article.
While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: (1) API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; (2) GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; (3) Different types of errors are enriched in different tasks, providing valuable insights for future improvements.
PLEASE WATCH THE FOLLOWING VIDEOS ON BIOGPT
This one entitled
Microsoft’s BioGPT Shows Promise as the Best Biomedical NLP
gives a good general description of this new MIT/Microsoft project and its usefullness in scanning 15 million articles on PubMed while returning ChatGPT like answers.
Please note one of the comments which is VERY IMPORTANT
bioGPT is difficult for non-developers to use, and Microsoft researchers seem to default that all users are proficient in Python and ML.
Much like Microsoft Azure it seems this BioGPT is meant for developers who have advanced programming skill. Seems odd then to be paying programmers multiK salaries when one or two Key Opinion Leaders from the medical field might suffice but I would be sure Microsoft will figure this out.
ALSO VIEW VIDEO
This is a talk from Microsoft on BioGPT
Other Relevant Articles on Natural Language Processing in BioInformatics, Healthcare and ChatGPT for Medicine on this Open Access Scientific Journal Include
The Human Genome Gets Fully Sequenced: A Simplistic Take on Century Long Effort
Curator: Stephen J. Williams, PhD
Article ID #295: The Human Genome Gets Fully Sequenced: A Simplistic Take on Century Long Effort. Published on 6/14/2022
WordCloud Image Produced by Adam Tubman
Ever since the hard work by Rosalind Franklin to deduce structures of DNA and the coincidental work by Francis Crick and James Watson who modeled the basic building blocks of DNA, DNA has been considered as the basic unit of heredity and life, with the “Central Dogma” (DNA to RNA to Protein) at its core. These were the discoveries in the early twentieth century, and helped drive the transformational shift of biological experimentation, from protein isolation and characterization to cloning protein-encoding genes to characterizing how the genes are expressed temporally, spatially, and contextually.
Rosalind Franklin, who’s crystolagraphic data led to determination of DNA structure. Shown as 1953 Time cover as Time person of the Year
Dr Francis Crick and James Watson in front of their model structure of DNA
Up to this point (1970s-mid 80s) , it was felt that genetic information was rather static, and the goal was still to understand and characterize protein structure and function while an understanding of the underlying genetic information was more important for efforts like linkage analysis of genetic defects and tools for the rapidly developing field of molecular biology. But the development of the aforementioned molecular biology tools including DNA cloning, sequencing and synthesis, gave scientists the idea that a whole recording of the human genome might be possible and worth the effort.
How the Human Genome Project Expanded our View of Genes Genetic Material and Biological Processes
The Human Genome Project (HGP) refers to the international 13-year effort, formally begun in October 1990 and completed in 2003, to discover all the estimated 20,000-25,000 human genes and make them accessible for further biological study. Another project goal was to determine the complete sequence of the 3 billion DNA subunits (bases in the human genome). As part of the HGP, parallel studies were carried out on selected model organisms such as the bacterium E. coli and the mouse to help develop the technology and interpret human gene function. The DOE Human Genome Program and the NIH National Human Genome Research Institute (NHGRI) together sponsored the U.S. Human Genome Project.
Please see the following for goals, timelines, and funding for this project
Timeline: Major Events in the Human Genome Project
It is interesting to note that multiple government legislation is credited for the funding of such a massive project including
Project Enabling Legislation
The Atomic Energy Act of 1946 (P.L. 79-585) provided the initial charter for a comprehensive program of research and development related to the utilization of fissionable and radioactive materials for medical, biological, and health purposes.
The Atomic Energy Act of 1954 (P.L. 83-706) further authorized the AEC “to conduct research on the biologic effects of ionizing radiation.”
The Energy Reorganization Act of 1974 (P.L. 93-438) provided that responsibilities of the Energy Research and Development Administration (ERDA) shall include “engaging in and supporting environmental, biomedical, physical, and safety research related to the development of energy resources and utilization technologies.”
The Federal Non-nuclear Energy Research and Development Act of 1974 (P.L. 93-577) authorized ERDA to conduct a comprehensive non-nuclear energy research, development, and demonstration program to include the environmental and social consequences of the various technologies.
The DOE Organization Act of 1977 (P.L. 95-91) mandated the Department “to assure incorporation of national environmental protection goals in the formulation and implementation of energy programs; and to advance the goal of restoring, protecting, and enhancing environmental quality, and assuring public health and safety,” and to conduct “a comprehensive program of research and development on the environmental effects of energy technology and program.”
It should also be emphasized that the project was not JUST funded through NIH but also Department of Energy
For a great read on Dr. Craig Ventnor with interviews with the scientist see Dr. Larry Bernstein’s excellent post The Human Genome Project
By 2003 we had gained much information about the structure of DNA, genes, exons, introns and allowed us to gain more insights into the diversity of genetic material and the underlying protein coding genes as well as many of the gene-expression regulatory elements. However there was much uninvestigated material dispersed between genes, the then called “junk DNA” and, up to 2003 not much was known about the function of this ‘junk DNA’. In addition there were two other problems:
The reference DNA used was actually from one person (Craig Ventor who was the lead initiator of the project)
Multiple gaps in the DNA sequence existed, and needed to be filled in
It is important to note that a tremendous amount of diversity of protein has been realized from both transcriptomic and proteomic studies. Although about 20 to 25,000 coding genes exist the human proteome contains about 600,000 proteoforms (due to alternative splicing, posttranslational modifications etc.)
This expansion of the proteoform via alternate splicing into isoforms, gene duplication to paralogs has been shown to have major effects on, for example, cellular signaling pathways (1)
However just recently it has been reported that the FULL human genome has been sequenced and is complete and verified. This was the focus of a recent issue in the journal Science.
Since its initial release in 2000, the human reference genome has covered only the euchromatic fraction of the genome, leaving important heterochromatic regions unfinished. Addressing the remaining 8% of the genome, the Telomere-to-Telomere (T2T) Consortium presents a complete 3.055 billion–base pair sequence of a human genome, T2T-CHM13, that includes gapless assemblies for all chromosomes except Y, corrects errors in the prior references, and introduces nearly 200 million base pairs of sequence containing 1956 gene predictions, 99 of which are predicted to be protein coding. The completed regions include all centromeric satellite arrays, recent segmental duplications, and the short arms of all five acrocentric chromosomes, unlocking these complex regions of the genome to variational and functional studies.
The current human reference genome was released by the Genome Reference Consortium (GRC) in 2013 and most recently patched in 2019 (GRCh38.p13) (1). This reference traces its origin to the publicly funded Human Genome Project (2) and has been continually improved over the past two decades. Unlike the competing Celera effort (3) and most modern sequencing projects based on “shotgun” sequence assembly (4), the GRC assembly was constructed from sequenced bacterial artificial chromosomes (BACs) that were ordered and oriented along the human genome by means of radiation hybrid, genetic linkage, and fingerprint maps. However, limitations of BAC cloning led to an underrepresentation of repetitive sequences, and the opportunistic assembly of BACs derived from multiple individuals resulted in a mosaic of haplotypes. As a result, several GRC assembly gaps are unsolvable because of incompatible structural polymorphisms on their flanks, and many other repetitive and polymorphic regions were left unfinished or incorrectly assembled (5).
Fig. 1. Summary of the complete T2T-CHM13 human genome assembly. (A) Ideogram of T2T-CHM13v1.1 assembly features. For each chromosome (chr), the following information is provided from bottom to top: gaps and issues in GRCh38 fixed by CHM13 overlaid with the density of genes exclusive to CHM13 in red; segmental duplications (SDs) (42) and centromeric satellites (CenSat) (30); and CHM13 ancestry predictions (EUR, European; SAS, South Asian; EAS, East Asian; AMR, ad-mixed American). Bottom scale is measured in Mbp. (B and C) Additional (nonsyntenic) bases in the CHM13 assembly relative to GRCh38 per chromosome, with the acrocentrics highlighted in black (B) and by sequence type (C). (Note that the CenSat and SD annotations overlap.) RepMask, RepeatMasker. (D) Total nongap bases in UCSC reference genome releases dating back to September 2000 (hg4) and ending with T2T-CHM13 in 2021. Mt/Y/Ns, mitochondria, chrY, and gaps.
Note in Figure 1D the exponential growth in genetic information.
Also very important is the ability to determine all the paralogs, isoforms, areas of potential epigenetic regulation, gene duplications, and transposable elements that exist within the human genome.
Analyses and resources
A number of companion studies were carried out to characterize the complete sequence of a human genome, including comprehensive analyses of centromeric satellites (30), segmental duplications (42), transcriptional (49) and epigenetic profiles (29), mobile elements (49), and variant calls (25). Up to 99% of the complete CHM13 genome can be confidently mapped with long-read sequencing, opening these regions of the genome to functional and variational analysis (23) (fig. S38 and table S14). We have produced a rich collection of annotations and omics datasets for CHM13—including RNA sequencing (RNA-seq) (30), Iso-seq (21), precision run-on sequencing (PRO-seq) (49), cleavage under targets and release using nuclease (CUT&RUN) (30), and ONT methylation (29) experiments—and have made these datasets available via a centralized University of California, Santa Cruz (UCSC), Assembly Hub genome browser (54).
To highlight the utility of these genetic and epigenetic resources mapped to a complete human genome, we provide the example of a segmentally duplicated region of the chromosome 4q subtelomere that is associated with facioscapulohumeral muscular dystrophy (FSHD) (55). This region includes FSHD region gene 1 (FRG1), FSHD region gene 2 (FRG2), and an intervening D4Z4 macrosatellite repeat containing the double homeobox 4 (DUX4) gene that has been implicated in the etiology of FSHD (56). Numerous duplications of this region throughout the genome have complicated past genetic analyses of FSHD.
The T2T-CHM13 assembly reveals 23 paralogs of FRG1 spread across all acrocentric chromosomes as well as chromosomes 9 and 20 (Fig. 5A). This gene appears to have undergone recent amplification in the great apes (57), and approximate locations of FRG1 paralogs were previously identified by FISH (58). However, only nine FRG1 paralogs are found in GRCh38, hampering sequence-based analysis.
Future of the human reference genome
The T2T-CHM13 assembly adds five full chromosome arms and more additional sequence than any genome reference release in the past 20 years (Fig. 1D). This 8% of the genome has not been overlooked because of a lack of importance but rather because of technological limitations. High-accuracy long-read sequencing has finally removed this technological barrier, enabling comprehensive studies of genomic variation across the entire human genome, which we expect to drive future discovery in human genomic health and disease. Such studies will necessarily require a complete and accurate human reference genome.
CHM13 lacks a Y chromosome, and homozygous Y-bearing CHMs are nonviable, so a different sample type will be required to complete this last remaining chromosome. However, given its haploid nature, it should be possible to assemble the Y chromosome from a male sample using the same methods described here and supplement the T2T-CHM13 reference assembly with a Y chromosome as needed.
Extending beyond the human reference genome, large-scale resequencing projects have revealed genomic variation across human populations. Our reanalyses of the 1KGP (25) and SGDP (42) datasets have already shown the advantages of T2T-CHM13, even for short-read analyses. However, these studies give only a glimpse of the extensive structural variation that lies within the most repetitive regions of the genome assembled here. Long-read resequencing studies are now needed to comprehensively survey polymorphic variation and reveal any phenotypic associations within these regions.
Although CHM13 represents a complete human haplotype, it does not capture the full diversity of human genetic variation. To address this bias, the Human Pangenome Reference Consortium (59) has joined with the T2T Consortium to build a collection of high-quality reference haplotypes from a diverse set of samples. Ideally, all genomes could be assembled at the quality achieved here, but automated T2T assembly of diploid genomes presents a difficult challenge that will require continued development. Until this goal is realized, and any human genome can be completely sequenced without error, the T2T-CHM13 assembly represents a more complete, representative, and accurate reference than GRCh38.
This paper was the focus of a Time article and their basis for making the lead authors part of their Time 100 people of the year.
The first human genome was mapped in 2001 as part of the Human Genome Project, but researchers knew it was neither complete nor completely accurate. Now, scientists have produced the most completely sequenced human genome to date, filling in gaps and correcting mistakes in the previous version.
The sequence is the most complete reference genome for any mammal so far. The findings from six new papers describing the genome, which were published in Science, should lead to a deeper understanding of human evolution and potentially reveal new targets for addressing a host of diseases.
A more precise human genome
“The Human Genome Project relied on DNA obtained through blood draws; that was the technology at the time,” says Adam Phillippy, head of genome informatics at the National Institutes of Health’s National Human Genome Research Institute (NHGRI) and senior author of one of the new papers. “The techniques at the time introduced errors and gaps that have persisted all of these years. It’s nice now to fill in those gaps and correct those mistakes.”
“We always knew there were parts missing, but I don’t think any of us appreciated how extensive they were, or how interesting,” says Michael Schatz, professor of computer science and biology at Johns Hopkins University and another senior author of the same paper.
The work is the result of the Telomere to Telomere consortium, which is supported by NHGRI and involves genetic and computational biology experts from dozens of institutes around the world. The group focused on filling in the 8% of the human genome that remained a genetic black hole from the first draft sequence. Since then, geneticists have been trying to add those missing portions bit by bit. The latest group of studies identifies about an entire chromosome’s worth of new sequences, representing 200 million more base pairs (the letters making up the genome) and 1,956 new genes.
NOTE: In 2001 many scientists postulated there were as much as 100,000 coding human genes however now we understand there are about 20,000 to 25,000 human coding genes. This does not however take into account the multiple diversity obtained from alternate splicing, gene duplications, SNPs, and chromosomal rearrangements.
Scientists were also able to sequence the long stretches of DNA that contained repeated sequences, which genetic experts originally thought were similar to copying errors and dismissed as so-called “junk DNA”. These repeated sequences, however, may play roles in certain human diseases. “Just because a sequence is repetitive doesn’t mean it’s junk,” says Eichler. He points out that critical genes are embedded in these repeated regions—genes that contribute to machinery that creates proteins, genes that dictate how cells divide and split their DNA evenly into their two daughter cells, and human-specific genes that might distinguish the human species from our closest evolutionary relatives, the primates. In one of the papers, for example, researchers found that primates have different numbers of copies of these repeated regions than humans, and that they appear in different parts of the genome.
“These are some of the most important functions that are essential to live, and for making us human,” says Eichler. “Clearly, if you get rid of these genes, you don’t live. That’s not junk to me.”
Deciphering what these repeated sections mean, if anything, and how the sequences of previously unsequenced regions like the centromeres will translate to new therapies or better understanding of human disease, is just starting, says Deanna Church, a vice president at Inscripta, a genome engineering company who wrote a commentary accompanying the scientific articles. Having the full sequence of a human genome is different from decoding it; she notes that currently, of people with suspected genetic disorders whose genomes are sequenced, about half can be traced to specific changes in their DNA. That means much of what the human genome does still remains a mystery.
The investigators in the Telomere to Telomere Consortium made the Time 100 People of the Year.
Michael Schatz, Karen Miga, Evan Eichler, and Adam Phillippy
Illustration by Brian Lutz for Time (Source Photos: Will Kirk—Johns Hopkins University; Nick Gonzales—UC Santa Cruz; Patrick Kehoe; National Human Genome Research Institute)
BY JENNIFER DOUDNA
MAY 23, 2022 6:08 AM EDT
Ever since the draft of the human genome became available in 2001, there has been a nagging question about the genome’s “dark matter”—the parts of the map that were missed the first time through, and what they contained. Now, thanks to Adam Phillippy, Karen Miga, Evan Eichler, Michael Schatz, and the entire Telomere-to-Telomere Consortium (T2T) of scientists that they led, we can see the full map of the human genomic landscape—and there’s much to explore.
In the scientific community, there wasn’t a consensus that mapping these missing parts was necessary. Some in the field felt there was already plenty to do using the data in hand. In addition, overcoming the technical challenges to getting the missing information wasn’t possible until recently. But the more we learn about the genome, the more we understand that every piece of the puzzle is meaningful.
I admire the
T2T group’s willingness to grapple with the technical demands of this project and their persistence in expanding the genome map into uncharted territory. The complete human genome sequence is an invaluable resource that may provide new insights into the origin of diseases and how we can treat them. It also offers the most complete look yet at the genetic script underlying the very nature of who we are as human beings.
Doudna is a biochemist and winner of the 2020 Nobel Prize in Chemistry
P. Scalia, A. Giordano, C. Martini, S. J. Williams, Isoform- and Paralog-Switching in IR-Signaling: When Diabetes Opens the Gates to Cancer. Biomolecules10, (Nov 30, 2020).
Gian M. Volpicelli is a senior writer at WIRED, where he covers cryptocurrency, decentralization, politics, and technology regulation. He received a master’s degree in journalism from City University of London after studying politics and international relations in Rome. He lives in London.
The duo behind Twitter Crypto say NFT profile pics and crypto tipping are just the beginning.
YOU MIGHT HAVE heard of crypto Twitter, the corner of the social network where accounts have Bored Apes as profile pictures, posts are rife with talk of tokens, blockchains, and buying the Bitcoin dip, and Elon Musk is venerated.
Then again, you might have heard of Twitter Crypto, the business unit devoted to developing the social network’s strategy for cryptocurrency, blockchains, and that grab-bag of decentralized technologies falling under the rubric of Web3. The team’s unveiling came in November 2021 via a tweet from the newly hired project lead, Tess Rinearson, a Berlin-based American computer scientist whose career includes stints at blockchain companies such as Tendermint and Interchain.
Rinearson joined Twitter at a crucial moment. Jack Dorsey, the vociferously pro-Bitcoin company CEO, would leave a few weeks later, to be replaced by CTO Parag Agrawal. Agrawal had played an instrumental role in Bluesky, a Twitter-backed project to create a protocol—possibly with blockchain components—to build decentralized social networks.
As crypto went mainstream globally and crypto Twitter burgeoned, the company tried to dominate the space. Under the stewardship of product manager Esther Crawford, in September 2021 Twitter introduced a “tipping” feature that helps creators on Twitter to receive Bitcoin contributions through Lightning—a network for fast Bitcoin payments. In January, Twitter allowed subscribers of Twitter’s premium service, Twitter Blue, to flaunt their NFTs as hexagonal profile pictures, through a partnership with NFT marketplace OpenSea.
Twitter Crypto is just getting started. While Rinearson works with people all across the company, her team is still under 10 people, although more hires are in the pipeline, judging from recent job postings. So it’s worth asking what is next. I caught up over a video call with Rinearson and Crawford to talk about where Twitter Crypto is headed.
The conversation has been edited for clarity and brevity.
WIRED: Let’s start with the basics. Why does Twitter have a crypto unit?
Tess Rinearson: We really see crypto—and what we’re now calling Web3— as something that could be this incredibly powerful tool that would unlock a lot for our users. The whole crypto world is like an internet of money, an internet of value that our users can potentially tap into to create new ways of owning their content, monetizing their content, owning their own identity, and even relating to each other.
One of my goals is to build Twitter’s crypto unit in such a way that it caters to communities that go beyond just that core crypto community. I love the crypto Twitter space, obviously—I’m a very proud member of the crypto community. And at the same time, I recognize that people who are really deep in the crypto space may not relate to concepts, like for instance blockchain’s immutability, in the same way that someone who’s less intensely involved might feel about those things.
So a lot of what we try to think about is, what can we learn from this group of people who are super engaged and really, really, creative? And then, how can we translate some of that stuff into a format or a mechanism or a product that’s a little bit more accessible to people who don’t have that background?
How are you learning from crypto Twitter? Do you just follow a lot of accounts, do you actually talk to them? How does that learning experience play out?
Esther Crawford: It’s a combination. We have an amazing research team that sets up panel interviews and surveys. But we’re also embedded in the community itself and follow a bunch of accounts, sit on Twitter spaces, go to conferences and events, engage with customers in that way. That’s the way the research piece of it works. But we also encounter it as end users: Twitter is the discovery platform today for all things crypto.
One of the things we do differently at Twitter is we build out in the open. And so this means having dialog with customers in real time—designers will take something that is very early-stage and post it as a tweet and then get real-time feedback. They’ll hop into spaces with product managers and engineering managers, talk about it live with real customers, and then incorporate that feedback into the designs and what ultimately we end up launching.
Rinearson: One of the things I wanted to make sure of before I came to Twitter was to know that we would be able to build features in the open and solicit feedback and show rough drafts. And so this is something I asked Parag Agrawal, who’s now the CEO, and was the person who hired me. Pretty early in the job interview process, I said this was going to be really important, and he said, “If you think it’s important to the success of this work, great, do it—thumbs up.” He also shares that openness.
As you said, Tess, you come from crypto. When you were out there, what did you think Twitter was getting right? What did you think Twitter was getting wrong?
Rinearson: I had been a Twitter power user for a really long time. The thing that I saw was a lot of aesthetic alignment between how Twitter exists in the world and the way that crypto exists in the world. Twitter has decentralized user experiences in its DNA. And, this is a bit cheesy, but people use Twitter sometimes in ways that they use a public blockchain, as a public database where everything’s time stamped and people can agree on what happened.
And for most people it’s open, it is there for public conversation. And then obviously it was also the place—a place—where the crypto community really found its footing. I think it’s been a place where an enormous amount of discovery happens, and education and learning for the whole community. I joined when there were some murmurings about Twitter starting to do crypto stuff, mostly stuff Esther had led actually, and I was excited to see where it was going. And then Twitter’s investment in Bluesky also gave me a lot of confidence.
Let’s talk about the two main things you have delivered so far: The crypto tipping feature and NFT pictures. Can you give me just a potted history of how each came about and why?
Crawford: Those are our first set of early explorations, and the reason why we started there was we really wanted to make sure that what we built benefited creators, their audiences, and then all the conversations that are happening on Twitter. For creators in particular, we know that they rely on platforms like Twitter to monetize and earn a living, and not all people are able to use traditional currencies. Not everybody has a traditional banking account setup.
And so we wanted to provide an opportunity for a borderless payment solution, and that’s why we decided to go ahead and use Bitcoin Lightning as our first big integration. One of the reasons we chose Bitcoin Lightning was also because of the low transaction fees. And we have Bitcoin and Ethereum addresses that you can also put in there [on your Twitter “tipping jar”]. We noticed that people were actually adding information about their crypto wallet addresses in their profiles. And so we wanted to make a more seamless experience, so that people could just tip through the platform, so that it felt native.
With NFT profile pictures, the way that came about was, again, looking at user behavior. People were adding NFTs that they owned as avatars, but you didn’t really know whether they owned those NFTs or not. So we decided to go ahead and build out that feature so that one could actually prove ownership.
That’s similar to how other things developed on Twitter, right? The hashtag, or even even the retweet, were initially just things users invented—by adding the # sign, or by pasting other users’ tweets—and then Twitter made that a feature.
Crawford: Yeah, exactly. Many of the best ideas come from watching user behavior on the platform, and then we just productize that.
Rinearson: Sometimes I’ve heard people call that the “help wanted signs,” and like, keeping an eye out for the “help wanted signs” across the platform. The NFT profile picture was a clear example of that.
How do all these things—these two things and possibly other crypto features coming further down the line—really help Twitter’s bottom line?
Crawford:With creator monetization our goal was to help creators get paid, not Twitter. But Twitter takes a really small cut of earnings. For more successful creators, we take a larger percentage. The way we think about this is, it is part of our revenue diversification.
Twitter today is a wholly ad-based business. In the future we imagine Twitter making money from a variety of different product areas. So Twitter Blue is one of those products—you can pay $2.99 a month and you get additional features, such as the NFT profile pictures. We really think that revenue diversification sits across a variety of areas, and creator monetization is one really small component of that.
As you said, these are just early experiments. Where is Twitter Crypto going next? What’s your vision for crypto technology’s role within Twitter?
Rinearson: The real trick here is to find the right parts of Twitter to decentralize, and to not try to decentralize everything at once—or, you know, make every user suddenly responsible for taking care of some private keys or something like that.
We have to find the right ways to open up some access to a decentralized economic layer, or give people ways that they can take their identity with them, without relying on a single centralized service.
We’re really early in these explorations, and even looking at things like Bitcoin tipping or the NFT profile pictures—we view those features as experiments themselves in a lot of ways and learning experiences. We’re learning things about how our users relate to these concepts, what they understand about them, what they find confusing, and what’s most useful to them. We really want to try to use this technology to bring utility to people and you know, not just like, sprinkle a little blockchain on it for the sake of it. So creator monetization is an area that I’m really excited about because I think there’s a really clear path forward. But again, we’re looking beyond that: We’re also looking at using crypto technology in fields like [digital] identity and [digital] ownership space and also figuring out how we can better serve crypto communities on the platform.
Are you going to put Twitter verified users’ blue ticks on a blockchain, then?
[Laughter]
No?
[More laughter]
OK, moving on. How does the kind of work you do dovetail with Bluesky’s plan to create a protocol for a decentralized social media platform? Is there any synergy there?
Rinearson: I have known Jay [Graber], the Bluesky lead, for a long time, and she and I are in pretty close contact. We check in with each other regularly and talk a lot about problems we might have in common that we’ll both need to solve. There’s an overlap looking at things in the identity area, but at the end of the day, it’s a separate project. She’s pretty focused on hiring her team, and they’re very focused on building a prototype of a protocol. That is different from what Esther and I are thinking about, which is like: There are all these blockchain protocols that exist, and we need to figure out how to make them useful and accessible for real people.
And when I say “real people,” I mean that in a sort of tongue-in-cheek contrast to hardcore crypto nerds like me. Jay is thinking much more about building for people who are creating decentralized networks. That is a very different focus area. Beyond that, I would just say it’s too early to say what Bluesky will mean for Twitter as a product. We are in touch, we have aligned values. But at the end of the day—separate teams.
Why is a centralized Silicon Valley company like Twitter the right place to start to bring more decentralization to internet users? Don’t we have just to start from scratch, build a new platform that is already decentralized?
Rinearson: I started in crypto in 2015, and I have a very vivid memory from those years of watching some of my coworkers—crypto engineers—trying to figure out how to secure some of their Bitcoin like before one of the Bitcoin forks [in which the Bitcoin blockchain split, creating new currencies], and they were panicking and freaking out. I thought there was no way that a normal person would be able to handle this in a way that would be safe. And so I was a little bit disillusioned with crypto, especially from a consumer perspective.
And then last year, I started seeing more interest from people whom I’ve known for a long time and weren’t crypto people. They were just starting to perk their heads up and take notice and start creating NFTs or start talking about DAOs. And I thought that that was interesting, that we were coming around a corner, and it might be time to start thinking about what this could mean for people beyond that hardcore crypto group.
And that was when Twitter reached out. You know, I don’t think that just any centralized platform would be able to bring crypto to the masses, so to speak. But I think Twitter has the right stuff. I think you have to meet people where they are with new technologies: find ways to onboard them and bring them along and show them what this might mean for them. make things accessible. And it’s really, really hard to do that with just a protocol. You need to have some kind of community, you need to have some kind of user base, you need to have some kind of platform. And Twitter’s just right there.
I don’t think I would say that a centralized platform is definitely the way to “bring crypto to the masses.” I do think that Twitter is the way to do it.
But why do the masses need crypto right now?
Rinearson: I don’t know that anyone needs crypto, and our goal is not to get everyone into crypto. Let’s be clear about that. But I do think that crypto is a potentially very powerful tool for people. And so I think what we are trying to do is show people how powerful it is and unlock those possibilities. It’s also possible that we create some products and features, where people actually don’t even really know what’s happening under the hood.
Like maybe we’re using crypto as a payment rail or again as an identity layer—users don’t necessarily need to know all of those implementation details. And that’s actually something we come back to a lot: What level of abstraction are we talking about with users? What story are we telling them about what’s happening under the hood? But yeah, I would just like to reiterate that the goal is not to just shovel everyone into crypto. We want to provide value for people.
Do you think there is a case for Twitter to launch its own cryptocurrency— a Twittercoin?
Rinearson: I think there’s a case for a lot of things—honestly, there’s a case for a lot of things. We’re trying to think really, really broadly about it.
Crawford: We’re actively exploring a lot of things. It’s not it’s not something we would be making an announcement about.
Rinearson: I think it is really important to stress that when you say “Twittercoin” you probably have a slightly different idea of what it is than we do. And are we exploring those ideas? Yes, we want to think about all of them. Do we have road maps for them? No. But are we trying to think about things really creatively and be really, really open-minded? Yes. We have this new economic technology that we think could unlock a lot of things for people. And we want to go down a bunch of rabbit holes and see what we come up with.
Gian M. Volpicelli is a senior writer at WIRED, where he covers cryptocurrency, decentralization, politics, and technology regulation. He received a master’s degree in journalism from City University of London after studying politics and international relations in Rome. He lives in London.
Highlighted Text in BLUE, BLACK, GREEN, RED by Aviva Lev-Ari, PhD, RN
In this article, I will list 9 free Harvard courses that you can take to learn data science from scratch. Feel free to skip any of these courses if you already possess knowledge of that subject.
Step 1: Programming
The first step you should take when learning data science is to learn to code. You can choose to do this with your choice of programming language?—?ideally Python or R.
If you’d like to learn R, Harvard offers an introductory R course created specifically for data science learners, called Data Science: R Basics.
This program will take you through R concepts like variables, data types, vector arithmetic, and indexing. You will also learn to wrangle data with libraries like dplyr and create plots to visualize data.
If you prefer Python, you can choose to take CS50’s Introduction to Programming with Python offered for free by Harvard. In this course, you will learn concepts like functions, arguments, variables, data types, conditional statements, loops, objects, methods, and more.
Both programs above are self-paced. However, the Python course is more detailed than the R program, and requires a longer time commitment to complete. Also, the rest of the courses in this roadmap are taught in R, so it might be worth learning R to be able to follow along easily.
Step 2: Data Visualization
Visualization is one of the most powerful techniques with which you can translate your findings in data to another person.
With Harvard’s Data Visualization program, you will learn to build visualizations using the ggplot2 library in R, along with the principles of communicating data-driven insights.
Step 3: Probability
In this course, you will learn essential probability concepts that are fundamental to conducting statistical tests on data. The topics taught include random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem.
The concepts above will be introduced with the help of a case study, which means that you will be able to apply everything you learned to an actual real-world dataset.
Step 4: Statistics
After learning probability, you can take this course to learn the fundamentals of statistical inference and modelling.
This program will teach you to define population estimates and margin of errors, introduce you to Bayesian statistics, and provide you with the fundamentals of predictive modeling.
Step 5: Productivity Tools (Optional)
I’ve included this project management course as optional since it isn’t directly related to learning data science. Rather, you will be taught to use Unix/Linux for file management, Github, version control, and creating reports in R.
The ability to do the above will save you a lot of time and help you better manage end-to-end data science projects.
Step 6: Data Pre-Processing
The next course in this list is called Data Wrangling, and will teach you to prepare data and convert it into a format that is easily digestible by machine learning models.
You will learn to import data into R, tidy data, process string data, parse HTML, work with date-time objects, and mine text.
As a data scientist, you often need to extract data that is publicly available on the Internet in the form of a PDF document, HTML webpage, or a Tweet. You will not always be presented with clean, formatted data in a CSV file or Excel sheet.
By the end of this course, you will learn to wrangle and clean data to come up with critical insights from it.
Step 7: Linear Regression
Linear regression is a machine learning technique that is used to model a linear relationship between two or more variables. It can also be used to identify and adjust the effect of confounding variables.
This course will teach you the theory behind linear regression models, how to examine the relationship between two variables, and how confounding variables can be detected and removed before building a machine learning algorithm.
Step 8: Machine Learning
Finally, the course you’ve probably been waiting for! Harvard’s machine learning program will teach you the basics of machine learning, techniques to mitigate overfitting, supervised and unsupervised modelling approaches, and recommendation systems.
Step 9: Capstone Project
After completing all the above courses, you can take Harvard’s data science capstone project, where your skills in data visualization, probability, statistics, data wrangling, data organization, regression, and machine learning will be assessed.
With this final project, you will get the opportunity to put together all the knowledge learnt from the above courses and gain the ability to complete a hands-on data science project from scratch.
Note: All the courses above are available on an online learning platform from edX and can be audited for free. If you want a course certificate, however, you will have to pay for one.
Medical Startups – Artificial Intelligence (AI) Startups in Healthcare
Reporters: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN and Shraga Rottem, MD, DSc,
The motivation for this post is two fold:
First, we are presenting an application of AI, NLP, DL to our own medical text in the Genomics space. Here we present the first section of Part 1 in the following book. Part 1 has six subsections that yielded 12 plots. The entire Book is represented by 38 x 2 = 76 plots.
Second, we bring to the attention of the e-Reader the list of 276 Medical Startups – Artificial Intelligence (AI) Startups in Healthcare as a hot universe of R&D activity in Human Health.
Third, to highlight one academic center with an AI focus
Dear friends of the ETH AI Center,
We would like to provide you with some exciting updates from the ETH AI Center and its growing community.
As the Covid-19 restrictions in Switzerland have recently been lifted, we would like to hear from you what kind of events you would like to see in 2022! Participate in the survey to suggest event formats and topics that you would enjoy being a part of. We are already excited to learn what we can achieve together this year.
We already have many interesting events coming up, we look forward to seeing you at our main and community events!
LPBI Group is applying AI for Medical Text Analysis with Machine Learning and Natural Language Processing: Statistical and Deep Learning
Our Book
Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
Medical Text Analysis of this Books shows the following results obtained by Madison Davis by applying Wolfram NLP for Biological Languages on our own Text. See below an Example:
With the explosive development of decentralized finance, we witness a phenomenal growth in tokenization of all kinds of assets, including equity, funds, debt, and real estate. By taking advantage of blockchain technology, digital assets are broadly grouped into fungible and non-fungible tokens (NFT). Here non-fungible tokens refer to those with unique and non-substitutable properties. NFT has widely attracted attention, and its protocols, standards, and applications are developing exponentially. It has been successfully applied to digital fantasy artwork, games, collectibles, etc. However, there is a lack of research in utilizing NFT in issues such as Intellectual Property. Applying for a patent and trademark is not only a time-consuming and lengthy process but also costly. NFT has considerable potential in the intellectual property domain. It can promote transparency and liquidity and open the market to innovators who aim to commercialize their inventions efficiently. The main objective of this paper is to examine the requirements of presenting intellectual property assets, specifically patents, as NFTs. Hence, we offer a layered conceptual NFT-based patent framework. Furthermore, a series of open challenges about NFT-based patents and the possible future directions are highlighted. The proposed framework provides fundamental elements and guidance for businesses in taking advantage of NFTs in real-world problems such as grant patents, funding, biotechnology, and so forth.
Introduction
Distributed ledger technologies (DLTs) such as blockchain are emerging technologies posing a threat to existing business models. Traditionally, most companies used centralized authorities in various aspects of their business, such as financial operations and setting up a trust with their counterparts. By the emergence of blockchain, centralized organizations can be substituted with a decentralized group of resources and actors. The blockchain mechanism was introduced in Bitcoin white paper in 2008, which lets users generate transactions and spend their money without the intervention of banks1. Ethereum, which is a second generation of blockchain, was introduced in 2014, allowing developers to run smart contracts on a distributed ledger. With smart contracts, developers and businesses can create financial applications that use cryptocurrencies and other forms of tokens for applications such as decentralized finance (DeFi), crowdfunding, decentralized exchanges, data records keeping, etc.2. Recent advances in distributed ledger technology have developed concepts that lead to cost reduction and the simplification of value exchange. Nowadays, by leveraging the advantages of blockchain and taking into account the governance issues, digital assets could be represented as tokens that existed in the blockchain network, which facilitates their transmission and traceability, increases their transparency, and improves their security3.
In the landscape of blockchain technology, there could be defined two types of tokens, including fungible tokens, in which all the tokens have equal value and non-fungible tokens (NFTs) that feature unique characteristics and are not interchangeable. Actually, non-fungible tokens are digital assets with a unique identifier that is stored on a blockchain4. NFT was initially suggested in Ethereum Improvement Proposals (EIP)-7215, and it was later expanded in EIP-11556. NFTs became one of the most widespread applications of blockchain technology that reached worldwide attention in early 2021. They can be digital representations of real-world objects. NFTs are tradable rights of digital assets (pictures, music, films, and virtual creations) where ownership is recorded in blockchain smart contracts7.
In particular, fungibility is the ability to exchange one with another of the same kind as an essential currency feature. The non-fungible token is unique and therefore cannot be substituted8. Recently, blockchain enthusiasts have indicated significant interest in various types of NFTs. They enthusiastically participate in NFT-related games or trades. CryptoPunks9, as one of the first NFTs on Ethereum, has developed almost 10,000 collectible punks and helped popularize the ERC-721 Standard. With the gamification of the breeding mechanics, CryptoKitties10 officially placed NFTs at the forefront of the market in 2017. CryptoKitties is an early blockchain game that enables users to buy, sell, collect, and digital breed cats. Another example is NBA Top Shot11, an NFT trading platform for digital short films buying and selling NBA events.
NFTs are developing remarkably and have provided many applications such as artist royalties, in-game assets, educational certificates, etc. However, it is a relatively new concept, and many areas of application need to be explored. Intellectual Property, including patent, trademark, and copyright, is an important area where NFTs can be applied usefully and solve existing problems.
Although NFTs have had many applications so far, it rarely has been used to solve real-world problems. In fact, an NFT is an exciting concept about Intellectual Property (IP). Applying for a patent and trademark is a time-consuming and lengthy process, but it is also costly. That is, registering a copyright or trademark may take months, while securing a patent can take years. On the contrary, with the help of unique features of NFT technology, it is possible to accelerate this process with considerable confidence and assurance about protecting the ownership of an IP. NFTs can offer IP protection while an applicant waits for the government to grant his/her more formal protection. It is cause for excitement that people who believe NFTs and Blockchain would make buying and selling patents easier, offering new opportunities for companies, universities, and inventors to make money off their innovations12. Patent holders will benefit from such innovation. It would give them the ability to ‘tokenize’ their patents. Because every transaction would be logged on a blockchain, it will be much easier to trace patent ownership changes. However, NFT would also facilitate the revenue generation of patents by democratizing patent licensing via NFT. NFTs support the intellectual property market by embedding automatic royalty collecting methods inside inventors’ works, providing them with financial benefits anytime their innovation is licensed. For example, each inventor’s patent would be minted as an NFT, and these NFTs would be joined together to form a commercial IP portfolio and minted as a compounded NFT. Each investor would automatically get their fair share of royalties whenever the licensing revenue is generated without tracking them down.
The authors in13, an overview of NFTs’ applications in different aspects such as gambling, games, and collectibles has been discussed. In addition4, provides a prototype for an event-tracking application based on Ethereum smart contract, and NFT as a solution for art and real estate auction systems is described in14. However, these studies have not discussed existing standards or a generalized architecture, enabling NFTs to be applied in diverse applications. For example, the authors in15 provide two general design patterns for creating and trading NFTs and discuss existing token standards for NFT. However, the proposed designs are limited to Ethereum, and other blockchains are not considered16. Moreover, different technologies for each step of the proposed procedure are not discussed. In8, the authors provide a conceptual framework for token designing and managing and discuss five views: token view, wallet view, transaction view, user interface view, and protocol view. However, no research provides a generalized conceptual framework for generating, recording, and tracing NFT based-IP, in blockchain network.
Even with the clear benefits that NFT-backed patents offer, there are a number of impediments to actually achieving such a system. For example, convincing patent owners to put current ownership records for their patents into NFTs poses an initial obstacle. Because there is no reliable framework for NFT-based patents, this paper provides a conceptual framework for presenting NFT-based patents with a comprehensive discussion on many aspects, ranging from the background, model components, token standards to application domains and research challenges. The main objective of this paper is to provide a layered conceptual NFT-based patent framework that can be used to register patents in a decentralized, tamper-proof, and trustworthy peer-to-peer network to trade and exchange them in the worldwide market. The main contributions of this paper are highlighted as follows:
Providing a comprehensive overview on tokenization of IP assets to create unique digital tokens.
Discussing the components of a distributed and trustworthy framework for minting NFT-based patents.
Highlighting a series of open challenges of NFT-based patents and enlightening the possible future trends.
The rest of the paper is structured as follows: “Background” section describes the Background of NFTs, Non-Fungible Token Standards. The NFT-based patent framework is described in “NFT-based patent framework” section. The Discussion and challenges are presented in “Discussion” section. Lastly, conclusions are given in “Conclusion” section.
Background
Colored Coins could be considered the first steps toward NFTs designed on the top of the Bitcoin network. Bitcoins are fungible, but it is possible to mark them to be distinguishable from the other bitcoins. These marked coins have special properties representing real-world assets like cars and stocks, and owners can prove their ownership of physical assets through the colored coins. By utilizing Colored Coins, users can transfer their marked coins’ ownership like a usual transaction and benefit from Bitcoin’s decentralized network17. Colored Coins had limited functionality due to the Bitcoin script limitations. Pepe is a green frog meme originated by Matt Furie that; users define tokens for Pepes and trade them through the Counterparty platform. Then, the tokens that were created by the picture of Pepes are decided if they are rare enough. Rare Pepe allows users to preserve scarcity, manage the ownership, and transfer their purchased Pepes.
In 2017, Larva Labs developed the first Ethereum-based NFT named CryptoPunks. It contains 10,000 unique human-like characters generated randomly. The official ownership of each character is stored in the Ethereum smart contract, and owners would trade characters. CryptoPunks project inspired CryptoKitties project. CryptoKitties attracts attention to NFT, and it is a pioneer in blockchain games and NFTs that launched in late 2017. CryptoKitties is a blockchain-based virtual game, and users collect and trade characters with unique features that shape kitties. This game was developed in Ethereum smart contract, and it pioneered the ERC-721 token, which was the first standard token in the Ethereum blockchain for NFTs. After the 2017 hype in NFTs, many projects started in this context. Due to increased attention to NFTs’ use-cases and growing market cap, different blockchains like EOS, Algorand, and Tezos started to support NFTs, and various marketplaces like SuperRare and Rarible, and OpenSea are developed to help users to trade NFTs. As mentioned, in general, assets are categorized into two main classes, fungible and non-fungible assets. Fungible assets are the ones that another similar asset can replace. Fungible items could have two main characteristics: replicability and divisibility.
Currency is a fungible item because a ten-dollar bill can be exchanged for another ten-dollar bill or divided into ten one-dollar bills. Despite fungible items, non-fungible items are unique and distinguishable. They cannot be divided or exchanged by another identical item. The first tweet on Twitter is a non-fungible item with mentioned characteristics. Another tweet cannot replace it, and it is unique and not divisible. NFT is a non-fungible cryptographic asset that is declared in a standard token format and has a unique set of attributes. Due to transparency, proof of ownership, and traceable transactions in the blockchain network, NFTs are created using blockchain technology.
Blockchain-based NFTs help enthusiasts create NFTs in the standard token format in blockchain, transfer the ownership of their NFTs to a buyer, assure uniqueness of NFTs, and manage NFTs completely. In addition, there are semi-fungible tokens that have characteristics of both fungible and non-fungible tokens. Semi-fungible tokens are fungible in the same class or specific time and non-fungible in other classes or different times. A plane ticket can be considered a semi-fungible token because a charter ticket can be exchanged by another charter ticket but cannot be exchanged by a first-class ticket. The concept of semi-fungible tokens plays the main role in blockchain-based games and reduces NFTs overhead. In Fig. 1, we illustrate fungible, non-fungible, and semi-fungible tokens. The main properties of NFTs are described as follows15:
Figure 1
Ownership: Because of the blockchain layer, the owner of NFT can easily prove the right of possession by his/her keys. Other nodes can verify the user’s ownership publicly.
Transferable: Users can freely transfer owned NFTs ownership to others on dedicated markets.
Transparency: By using blockchain, all transactions are transparent, and every node in the network can confirm and trace the trades.
Fraud Prevention: Fraud is one of the key problems in trading assets; hence, using NFTs ensures buyers buy a non-counterfeit item.
Immutability: Metadata, token ID, and history of transactions of NFTs are recorded in a distributed ledger, and it is impossible to change the information of the purchased NFTs.
Non-fungible standards
Ethereum blockchain was pioneered in implementing NFTs. ERC-721 token was the first standard token accepted in the Ethereum network. With the increase in popularity of the NFTs, developers started developing and enhancing NFTs standards in different blockchains like EOS, Algorand, and Tezos. This section provides a review of implemented NFTs standards on the mentioned blockchains.
Ethereum
ERC-721 was the first Standard for NFTs developed in Ethereum, a free and open-source standard. ERC-721 is an interface that a smart contract should implement to have the ability to transfer and manage NFTs. Each ERC-721 token has unique properties and a different Token Id. ERC-721 tokens include the owner’s information, a list of approved addresses, a transfer function that implements transferring tokens from owner to buyer, and other useful functions5.
In ERC-721, smart contracts can group tokens with the same configuration, and each token has different properties, so ERC-721 does not support fungible tokens. However, ERC-1155 is another standard on Ethereum developed by Enjin and has richer functionalities than ERC-721 that supports fungible, non-fungible, and semi-fungible tokens. In ERC-1155, IDs define the class of assets. So different IDs have a different class of assets, and each ID may contain different assets of the same class. Using ERC-1155, a user can transfer different types of tokens in a single transaction and mix multiple fungible and non-fungible types of tokens in a single smart contract6. ERC-721 and ERC-1155 both support operators in which the owner can let the operator originate transferring of the token.
EOSIO
EOSIO is an open-source blockchain platform released in 2018 and claims to eliminate transaction fees and increase transaction throughput. EOSIO differs from Ethereum in the wallet creation algorithm and procedure of handling transactions. dGood is a free standard developed in the EOS blockchain for assets, and it focuses on large-scale use cases. It supports a hierarchical naming structure in smart contracts. Each contract has a unique symbol and a list of categories, and each category contains a list of token names. Therefore, a single contract in dGoods could contain many tokens, which causes efficiency in transferring a group of tokens. Using this hierarchy, dGoods supports fungible, non-fungible, and semi-fungible tokens. It also supports batch transferring, where the owner can transfer many tokens in one operation18.
Algorand
Algorand is a new high-performance public blockchain launched in 2019. It provides scalability while maintaining security and decentralization. It supports smart contracts and tokens for representing assets19. Algorand defines Algorand Standard Assets (ASA) concept to create and manage assets in the Algorand blockchain. Using ASA, users are able to define fungible and non-fungible tokens. In Algorand, users can create NFTs or FTs without writing smart contracts, and they should run just a single transaction in the Algorand blockchain. Each transaction contains some mutable and immutable properties20.
Each account in Algorand can create up to 1000 assets, and for every asset, an account creates or receives, the minimum balance of the account increases by 0.1 Algos. Also, Algorand supports fractional NFTs by splitting an NFT into a group of divided FTs or NFTs, and each part can be exchanged dependently21. Algorand uses a Clawback Address that operates like an operator in ERC-1155, and it is allowed to transfer tokens of an owner who has permitted the operator.
Tezos
Tezos is another decentralized open-source blockchain. Tezos supports the meta-consensus concept. In addition to using a consensus protocol on the ledger’s state like Bitcoin and Ethereum, It also attempts to reach a consensus about how nodes and the protocol should change or upgrade22. FA2 (TZIP-12) is a standard for a unified token contract interface in the Tezos blockchain. FA2 supports different token types like fungible, non-fungible, and fractionalized NFT contracts. In Tezos, tokens are identified with a token contract address and token ID pair. Also, Tezos supports batch token transferring, which reduces the cost of transferring multiple tokens.
Flow
Flow was developed by Dapper Labs to remove the scalability limitation of the Ethereum blockchain. Flow is a fast and decentralized blockchain that focuses on games and digital collectibles. It improves throughput and scalability without sharding due to its architecture. Flow supports smart contracts using Cadence, which is a resource-oriented programming language. NFTs can be described as a resource with a unique id in Cadence. Resources have important rules for ownership management; that is, resources have just one owner and cannot be copied or lost. These features assure the NFT owner. NFTs’ metadata, including images and documents, can be stored off-chain or on-chain in Flow. In addition, Flow defines a Collection concept, in which each collection is an NFT resource that can include a list of resources. It is a dictionary that the key is resource id, and the value is corresponding NFT.
The collection concept provides batch transferring of NFTs. Besides, users can define an NFT for an FT. For instance, in CryptoKitties, a unique cat as an NFT can own a unique hat (another NFT). Flow uses Cadence’s second layer of access control to allow some operators to access some fields of the NFT23. In Table 1, we provide a comparison between explained standards. They are compared in support of fungible-tokens, non-fungible tokens, batch transferring that owner can transform multiple tokens in one operation, operator support in which the owner can approve an operator to originate token transfer, and fractionalized NFTs that an NFT can divide to different tokens and each exchange dependently.Table 1 Comparing NFT standards.
In this section, we propose a framework for presenting NFT-based patents. We describe details of the proposed distributed and trustworthy framework for minting NFT-based patents, as shown in Fig. 2. The proposed framework includes five main layers: Storage Layer, Authentication Layer, Verification Layer, Blockchain Layer, and Application Layer. Details of each layer and the general concepts are presented as follows.
Figure 2
Storage layer
The continuous rise of the data in blockchain technology is moving various information systems towards the use of decentralized storage networks. Decentralized storage networks were created to provide more benefits to the technological world24. Some of the benefits of using decentralized storage systems are explained: (1) Cost savings are achieved by making optimal use of current storage. (2) Multiple copies are kept on various nodes, avoiding bottlenecks on central servers and speeding up downloads. This foundation layer implicitly provides the infrastructure required for the storage. The items on NFT platforms have unique characteristics that must be included for identification.
Non-fungible token metadata provides information that describes a particular token ID. NFT metadata is either represented on the On-chain or Off-chain. On-chain means direct incorporation of the metadata into the NFT’s smart contract, which represents the tokens. On the other hand, off-chain storage means hosting the metadata separately25.
Blockchains provide decentralization but are expensive for data storage and never allow data to be removed. For example, because of the Ethereum blockchain’s current storage limits and high maintenance costs, many projects’ metadata is maintained off-chain. Developers utilize the ERC721 Standard, which features a method known as tokenURI. This method is implemented to let applications know the location of the metadata for a specific item. Currently, there are three solutions for off-chain storage, including InterPlanetary File System (IPFS), Pinata, and Filecoin.
IPFS
InterPlanetary File System (IPFS) is a peer-to-peer hypermedia protocol for decentralized media content storage. Because of the high cost of storing media files related to NFTS on Blockchain, IPFS can be the most affordable and efficient solution. IPFS combines multiple technologies inspired by Gita and BitTorrent, such as Block Exchange System, Distributed Hash Tables (DHT), and Version Control System26. On a peer-to-peer network, DHT is used to coordinate and maintain metadata.
In other words, the hash values must be mapped to the objects they represent. An IPFS generates a hash value that starts with the prefix {Q}_{m} and acts as a reference to a specific item when storing an object like a file. Objects larger than 256 KB are divided into smaller blocks up to 256 KB. Then a hash tree is used to interconnect all the blocks that are a part of the same object. IPFS uses Kamdelia DHT. The Block Exchange System, or BitSwap, is a BitTorrent-inspired system that is used to exchange blocks. It is possible to use asymmetric encryption to prevent unauthorized access to stored content on IPFS27.
Pinata
Pinata is a popular platform for managing and uploading files on IPFS. It provides secure and verifiable files for NFTs. Most data is stored off-chain by most NFTs, where a URL of the data is pointed to the NFT on the blockchain. The main problem here is that some information in the URL can change.
This indicates that an NFT supposed to describe a certain patent can be changed without anyone knowing. This defeats the purpose of the NFT in the first place. This is where Pinata comes in handy. Pinata uses the IPFS to create content-addressable hashes of data, also known as Content-Identifiers (CIDs). These CIDs serve as both a way of retrieving data and a means to ensure data validity. Those looking to retrieve data simply ask the IPFS network for the data associated with a certain CID, and if any node on the network contains that data, it will be returned to the requester. The data is automatically rehashed on the requester’s computer when the requester retrieves it to make sure that the data matches back up with the original CID they asked for. This process ensures the data that’s received is exactly what was asked for; if a malicious node attempts to send fake data, the resulting CID on the requester’s end will be different, alerting the requester that they’re receiving incorrect data28.
Filecoin
Another decentralized storage network is Filecoin. It is built on top of IPFS and is designed to store the most important data, such as media files. Truffle Suite has also launched NFT Development Template with Filecoin Box. NFT.Storage (Free Decentralized Storage for NFTs)29 allows users to easily and securely store their NFT content and metadata using IPFS and Filecoin. NFT.Storage is a service backed by Protocol Labs and Pinata specifically for storing NFT data. Through content addressing and decentralized storage, NFT.Storage allows developers to protect their NFT assets and associated metadata, ensuring that all NFTs follow best practices to stay accessible for the long term. NFT.Storage makes it completely frictionless to mint NFTs following best practices through resilient persistence on IPFS and Filecoin. NFT.Storage allows developers to quickly, safely, and for free store NFT data on decentralized networks. Anyone can leverage the power of IPFS and Filecoin to ensure the persistence of their NFTs. The details of this system are stated as follows30:
Content addressing
Once users upload data on NFT.Storage, They receive a CID, which is an IPFS hash of the content. CIDs are the data’s unique fingerprints, universal addresses that can be used to refer to it regardless of how or where it is stored. Using CIDs to reference NFT data avoids problems such as weak links and “rug pulls” since CIDs are generated from the content itself.
Provable storage
NFT.Storage uses Filecoin for long-term decentralized data storage. Filecoin uses cryptographic proofs to assure the NFT data’s durability and persistence over time.
Resilient retrieval
This data stored via IPFS and Filecoin can be fetched directly in the browser via any public IPFS.
Authentication Layer
The second layer is the authentication layer, which we briefly highlight its functions in this section. The Decentralized Identity (DID) approach assists users in collecting credentials from a variety of issuers, such as the government, educational institutions, or employers, and saving them in a digital wallet. The verifier then uses these credentials to verify a person’s validity by using a blockchain-based ledger to follow the “identity and access management (IAM)” process. Therefore, DID allows users to be in control of their identity. A lack of NFT verifiability also causes intellectual property and copyright infringements; of course, the chain of custody may be traced back to the creator’s public address to check whether a similar patent is filed using that address. However, there is no quick and foolproof way to check an NFTs creator’s legitimacy. Without such verification built into the NFT, an NFT proves ownership only over that NFT itself and nothing more.
Self-sovereign identity (SSI)31 is a solution to this problem. SSI is a new series of standards that will guide a new identity architecture for the Internet. With a focus on privacy, security interoperability, SSI applications use public-key cryptography with public blockchains to generate persistent identities for people with private and selective information disclosure. Blockchain technology offers a solution to establish trust and transparency and provide a secure and publicly verifiable KYC (Know Your Customer). The blockchain architecture allows you to collect information from various service providers into a single cryptographically secure and unchanging database that does not need a third party to verify the authenticity of the information.
The proposed platform generates patents-related smart contracts acting as a program that runs on the blockchain to receive and send transactions. They are unalterable privately identifying clients with a thorough KYC process. After KYC approval, then mint an NFT on the blockchain as a certificate of verification32. This article uses a decentralized authentication solution at this layer for authentication. This solution has been used for various applications in the field of the blockchain (exp: smart city, Internet of Things, etc.33, 34, but we use it here for the proposed framework (patent as NFTs). Details of this solution will be presented in the following.
Decentralized authentication
This section presents the authentication layer similar35 to build validated communication in a secure and decentralized manner via blockchain technology. As shown in Fig. 3, the authentication protocol comprises two processes, including registration and login.
Figure 3
Registration
In the registration process of a suggested authentication protocol, we first initialize a user’s public key as their identity key (UserName). Then, we upload this identity key on a blockchain, in which transactions can be verified later by other users. Finally, the user generates an identity transaction.
Login
After registration, a user logs in to the system. The login process is described as follows:
1. The user commits identity information and imports their secret key into the service application to log in.
2. A user who needs to log in sends a login request to the network’s service provider.
3. The service provider analyzes the login request, extracts the hash, queries the blockchain, and obtains identity information from an identity list (identity transactions).
4. The service provider responds with an authentication request when the above process is completed. A timestamp (to avoid a replay attack), the user’s UserName, and a signature are all included in the authentication request.
5. The user creates a signature with five parameters: timestamp, UserName, and PK, as well as the UserName and PK of the service provider. The user authentication credential is used as the signature.
6. The service provider verifies the received information, and if the received information is valid, the authentication succeeds; otherwise, the authentication fails, and the user’s login is denied.
The World Intellectual Property Organization (WIPO) and multiple target patent offices in various nations or regions should assess a patent application, resulting in inefficiency, high costs, and uncertainty. This study presented a conceptual NFT-based patent framework for issuing, validating, and sharing patent certificates. The platform aims to support counterfeit protection as well as secure access and management of certificates according to the needs of learners, companies, education institutions, and certification authorities.
Here, the certification authority (CA) is used to authenticate patent offices. The procedure will first validate a patent if it is provided with a digital certificate that meets the X.509 standard. Certificate authorities are introduced into the system to authenticate both the nodes and clients connected to the blockchain network.
Verification layer
In permissioned blockchains, just identified nodes can read and write in the distributed ledger. Nodes can act in different roles and have various permissions. Therefore, a distributed system can be designed to be the identified nodes for patent granting offices. Here the system is described conceptually at a high level. Figure 4 illustrates the sequence diagram of this layer. This layer includes four levels as below:
Figure 4
Digitalization
For a patent to publish as an NFT in the blockchain, it must have a digitalized format. This level is the “filling step” in traditional patent registering. An application could be designed in the application layer to allow users to enter different patent information online.
Recording
Patents provide valuable information and would bring financial benefits for their owner. If they are publicly published in a blockchain network, miners may refuse the patent and take the innovation for themselves. At least it can weaken consensus reliability and encourage miners to misbehave. The inventor should record his innovation privately first using proof of existence to prevent this. The inventor generates the hash of the patent document and records it in the blockchain. As soon as it is recorded in the blockchain, the timestamp and the hash are available for others publicly. Then, the inventor can prove the existence of the patent document whenever it is needed.
Furthermore, using methods like Decision Thinking36, an inventor can record each phase of patent development separately. In each stage, a user generates the hash of the finished part and publishes the hash regarding the last part’s hash. Finally, they have a coupled series of hashes that indicate patent development, and they can prove the existence of each phase using the original related documents. This level should be done to prevent others from abusing the patent and taking it for themselves. The inventor can make sure that their patent document is recorded confidentially and immutably37.
Different hash algorithms exist with different architecture, time complexity, and security considerations. Hash functions should satisfy two main requirements: Pre-Image Resistance: This means that it should be computationally hard to find the input of a hash function while the output and the hash algorithm are known publicly. Collision Resistance: This means that it is computationally hard to find two arbitrary inputs, x, and y, that have the same hash output. These requirements are vital for recording patents. First, the hash function should be Pre-Image Resistance to make it impossible for others to calculate the patent documentation. Otherwise, everybody can read the patent, even before its official publication. Second, the hash function should satisfy Collision Resistance to preclude users from changing their document after recording. Otherwise, users can upload another document, and after a while, they can replace it with another one.
There are various hash algorithms, and MD and SHA families are the most useful algorithms. According to38, Collisions have been found for MD2, MD4, MD5, SHA-0, and SHA-1 hash functions. Hence, they cannot be a good choice for recording patents. SHA2 hash algorithm is secure, and no collision has been found. Although SHA2 is noticeably slower than prior hash algorithms, the recording phase is not highly time-sensitive. So, it is a better choice and provides excellent security for users.
Validating
In this phase, the inventors first create NFT for their patents and publish it to the miners/validators. Miners are some identified nodes that validate NFTs to record in the blockchain. Due to the specialization of the patent validation, miners cannot be inexpert public persons. In addition, patent offices are not too many to make the network fully decentralized. Therefore, the miners can be related specialist persons that are certified by the patent offices. They should receive a digital certificate from patent offices that show their eligibility to referee a patent.
Digital certificate
Digital certificates are digital credentials used to verify networked entities’ online identities. They usually include a public key as well as the owner’s identification. They are issued by Certification Authorities (CAs), who must verify the certificate holder’s identity. Certificates contain cryptographic keys for signing, encryption, and decryption. X.509 is a standard that defines the format of public-key certificates and is signed by a certificate authority. X.509 standard has multiple fields, and its structure is shown in Fig. 5. Version: This field indicated the version of the X.509 standard. X.509 contains multiple versions, and each version has a different structure. According to the CA, validators can choose their desired version. Serial Number: It is used to distinguish a certificate from other certificates. Thus, each certificate has a unique serial number. Signature Algorithm Identifier: This field indicates the cryptographic encryption algorithm used by a certificate authority. Issuer Name: This field indicates the issuer’s name, which is generally certificate authority. Validity Period: Each certificate is valid for a defined period, defined as the Validity Period. This limited period partly protects certificates against exposing CA’s private key. Subject Name: Name of the requester. In our proposed framework, it is the validator’s name. Subject Public Key Info: Shows the CA’s or organization’s public key that issued the certificate. These fields are identical among all versions of the X.509 standard39.
Figure 5
Certificate authority
A Certificate Authority (CA) issues digital certificates. CAs encrypt the certificate with their private key, which is not public, and others can decrypt the certificates containing the CA’s public key.
Here, the patent office creates a certificate for requested patent referees. The patent office writes the information of the validator in their certificate and encrypts it with the patent offices’ private key. The validator can use the certificate to assure others about their eligibility. Other nodes can check the requesting node’s information by decrypting the certificate using the public key of the patent office. Therefore, persons can join the network’s miners/validators using their credentials. In this phase, miners perform Formal Examinations, Prior Art Research, and Substantive Examinations and vote to grant or refuse the patent.
Miners perform a consensus about the patent and record the patent in the blockchain. After that, the NFT is recorded in the blockchain with corresponding comments in granting or needing reformations. If the miners detect the NFT as a malicious request, they do not record it in the blockchain.
Blockchain layer
This layer plays as a middleware between the Verification Layer and Application Layer in the patents as NFTs architecture. The main purpose of the blockchain layer in the proposed architecture is to provide IP management. We find that transitioning to a blockchain-based patent as a NFTs records system enables many previously suggested improvements to current patent systems in a flexible, scalable, and transparent manner.
On the other hand, we can use multiple blockchain platforms, including Ethereum, EOS, Flow, and Tezos. Blockchain Systems can be mainly classified into two major types: Permissionless (public) and Permissioned (private) Blockchains based on their consensus mechanism. In a public blockchain, any node can participate in the peer-to-peer network, where the blockchain is fully decentralized. A node can leave the network without any consent from the other nodes in the network.
Bitcoin is one of the most popular examples that fall under the public and permissionless blockchain. Proof of Work (POW), Proof-of-Stake (POS), and directed acyclic graph (DAG) are some examples of consensus algorithms in permissionless blockchains. Bitcoin and Ethereum, two famous and trustable blockchain networks, use the PoW consensus mechanism. Blockchain platforms like Cardano and EOS adopt the PoS consensus40.
Nodes require specific access or permission to get network authentication in a private blockchain. Hyperledger is among the most popular private blockchains, which allow only permissioned members to join the network after authentication. This provides security to a group of entities that do not completely trust one another but wants to achieve a common objective such as exchanging information. All entities of a permissioned blockchain network can use Byzantine-fault-tolerant (BFT) consensus. The Fabric has a membership identity service that manages user IDs and verifies network participants.
Therefore, members are aware of each other’s identity while maintaining privacy and secrecy because they are unaware of each other’s activities41. Due to their more secure nature, private blockchains have sparked a large interest in banking and financial organizations, believing that these platforms can disrupt current centralized systems. Hyperledger, Quorum, Corda, EOS are some examples of permissioned blockchains42.
Reaching consensus in a distributed environment is a challenge. Blockchain is a decentralized network with no central node to observe and check all transactions. Thus, there is a need to design protocols that indicate all transactions are valid. So, the consensus algorithms are considered as the core of each blockchain43. In distributed systems, the consensus has become a problem in which all network members (nodes) agree on accept or reject of a block. When all network members accept the new block, it can append to the previous block.
As mentioned, the main concern in the blockchains is how to reach consensus among network members. A wide range of consensus algorithms has been designed in which each of them has its own pros and cons42. Blockchain consensus algorithms are mainly classified into three groups shown in Table 2. As the first group, proof-based consensus algorithms require the nodes joining the verifying network to demonstrate their qualification to do the appending task. The second group is voting-based consensus that requires validators in the network to share their results of validating a new block or transaction before making the final decision. The third group is DAG-based consensus, a new class of consensus algorithms. These algorithms allow several different blocks to be published and recorded simultaneously on the network.Table 2 Consensus algorithms in blockchain networks.
The proposed patent as the NFTs platform that builds blockchain intellectual property empowers the entire patent ecosystem. It is a solution that removes barriers by addressing fundamental issues within the traditional patent ecosystem. Blockchain can efficiently handle patents and trademarks by effectively reducing approval wait time and other required resources. The user entities involved in Intellectual Property management are Creators, Patent Consumers, and Copyright Managing Entities. Users with ownership of the original data are the patent creators, e.g., inventors, writers, and researchers. Patent Consumers are the users who are willing to consume the content and support the creator’s work. On the other hand, Users responsible for protecting the creators’ Intellectual Property are the copyright management entities, e.g., lawyers. The patents as NFTs solution for IP management in blockchain layer works by implementing the following steps62:
Creators sign up to the platform
Creators need to sign up on the blockchain platform to patent their creative work. The identity information will be required while signing up.
Creators upload IP on the blockchain network
Now, add an intellectual property for which the patent application is required. The creator will upload the information related to IP and the data on the blockchain network. Blockchain ensures traceability and auditability to prevent data from duplicity and manipulation. The patent becomes visible to all network members once it is uploaded to the blockchain.
Consumers generate request to use the content
Consumers who want to access the content must first register on the blockchain network. After Signing up, consumers can ask creators to grant access to the patented content. Before the patent owner authorizes the request, a Smart Contract is created to allow customers to access information such as the owner’s data. Furthermore, consumers are required to pay fees in either fiat money or unique tokens in order to use the creator’s original information. When the creator approves the request, an NDA (Non-Disclosure Agreement) is produced and signed by both parties. Blockchain manages the agreement and guarantees that all parties agree to the terms and conditions filed.
Patent management entities leverage blockchain to protect copyrights and solve related disputes
Blockchain assists the patent management entities in resolving a variety of disputes that may include: sharing confidential information, establishing proof of authorship, transferring IP rights, and making defensive publications, etc. Suppose a person used an Invention from a patent for his company without the inventor’s consent. The inventor can report it to the patent office and claim that he is the owner of that invention.
Application layer
The patent Platform Global Marketplace technology would allow many enterprises, governments, universities, and Small and medium-sized enterprises (SMEs) worldwide to tokenize patents as NFTs to create an infrastructure for storing patent records on a blockchain-based network and developing a decentralized marketplace in which patent holders would easily sell or otherwise monetize their patents. The NFTs-based patent can use smart contracts to determine a set price for a license or purchase.
Any buyer satisfied with the conditions can pay and immediately unlock the rights to the patent without either party ever having to interact directly. While patents are currently regulated jurisdictionally around the world, a blockchain-based patent marketplace using NFTs can reduce the geographical barriers between patent systems using as simple a tool as a search query. The ease of access to patents globally can help aspiring inventors accelerate the innovative process by building upon others’ patented inventions through licenses. There are a wide variety of use cases for patent NFTs such as SMEs, Patent Organization, Grant & Funding, and fundraising/transferring information relating to patents. These applications keep growing as time progresses, and we are constantly finding new ways to utilize these tokens. Some of the most commonly used applications can be seen as follows.
SMEs
The aim is to move intellectual property assets onto a digital, centralized, and secure blockchain network, enabling easier commercialization of patents, especially for small or medium enterprises (SMEs). Smart contracts can be attached to NFTs so terms of use and ownership can be outlined and agreed upon without incurring as many legal fees as traditional IP transfers. This is believed to help SMEs secure funding, as they could more easily leverage the previously undisclosed value of their patent portfolios63.
Transfer ownership of patents
NFTs can be used to transfer ownership of patents. The blockchain can be used to keep track of patent owners, and tokens would include self-executing contracts that transfer the legal rights associated with patents when the tokens are transferred. A partnership between IBM and IPwe has spearheaded the use of NFTs to secure patent ownership. These two companies have teamed together to build the infrastructure for an NFT-based patent marketplace.
Discussion
There are exciting proposals in the legal and economic literature that suggest seemingly straightforward solutions to many of the issues plaguing current patent systems. However, most solutions would constitute major administrative disruptions and place significant and continuous financial burdens on patent offices or their users. An NFT-based patents system not only makes many of these ideas administratively feasible but can also be examined in a step-wise, scalable, and very public manner.
Furthermore, NFT-based patents may facilitate reliable information sharing among offices and patentees worldwide, reducing the burden on examiners and perhaps even accelerating harmonization efforts. NFT-based patents also have additional transparency and archival attributes baked in. A patent should be a privilege bestowed on those who take resource-intensive risks to explore the frontier of technological capabilities. As a reward for their achievements, full transparency of these rewards is much public interest. It is a society that pays for administrative and economic inefficiencies that exist in today’s systems. NFT-based patents can enhance this transparency. From an organizational perspective, an NFT-based patent can remove current bottlenecks in patent processes by making these processes more efficient, rapid, and convenient for applicants without compromising the quality of granted patents.
The proposed framework encounters some challenges that should be solved to reach a developed patent verification platform. First, technical problems are discussed. The consensus method that is used in the verification layer is not addressed in detail. Due to the permissioned structure of miners in the NFT-based patents, consensus algorithms like PBFT, Federated Consensus, and Round Robin Consensus are designed for permissioned blockchains can be applied. Also, miners/validators spend some time validating the patents; hence a protocol should be designed to profit them. Some challenges like proving the miners’ time and effort, the price that inventors should pay to miners, and other economic trade-offs should be considered.
Different NFT standards were discussed. If various patent services use NFT standards, there will be some cross-platform problems. For instance, transferring an NFT from Ethereum blockchain (ERC-721 token) to EOS blockchain is not a forward and straight work and needs some considerations. Also, people usually trade NFTs in marketplaces such as Rarible and OpenSea. These marketplaces are centralized and may prompt some challenges because of their centralized nature. Besides, there exist some other types of challenges. For example, the novelty of NFT-based patents and blockchain services.
Blockchain-based patent service has not been tested before. The patent registration procedure and concepts of the Patent as NFT system may be ambiguous for people who still prefer conventional centralized patent systems over decentralized ones. It should be noted that there are some problems in the mining part. Miners should receive certificates from the accepted organizations. Determining these organizations and how they accept referees as validators need more consideration. Some types of inventions in some countries are prohibited, and inventors cannot register them. In NFT-based patents, inventors can register their patents publicly, and maybe some collisions occur between inventors and the government. There exist some misunderstandings about NFT’s ownership rights. It is not clear that when a person buys an NFT, which rights are given to them exactly; for instance, they have property rights or have moral rights, too.
Conclusion
Blockchain technology provides strong timestamping, the potential for smart contracts, proof-of-existence. It enables creating a transparent, distributed, cost-effective, and resilient environment that is open to all and where each transaction is auditable. On the other hand, blockchain is a definite boon to the IP industry, benefitting patent owners. When blockchain technology’s intrinsic characteristics are applied to the IP domain, it helps copyrights. This paper provided a conceptual framework for presenting an NFT-based patent with a comprehensive discussion of many aspects: background, model components, token standards to application areas, and research challenges. The proposed framework includes five main layers: Storage Layer, Authentication Layer, Verification Layer, Blockchain Layer, and Application. The primary purpose of this patent framework was to provide an NFT-based concept that could be used to patent a decentralized, anti-tamper, and reliable network for trade and exchange around the world. Finally, we addressed several open challenges to NFT-based inventions.
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This work has been partially supported by CAS President’s International Fellowship Initiative, China [grant number 2021VTB0002, 2021] and National Natural Science Foundation of China (No. 61902385).
Author information
Affiliations
Department of Industrial Management, Yazd University, Yazd City, IranSeyed Mojtaba Hosseini Bamakan
Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan City, IranNasim Nezhadsistani
School of Electrical and Computer Engineering, University of Tehran, Tehran City, IranOmid Bodaghi
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, ChinaSeyed Mojtaba Hosseini Bamakan & Qiang Qu
From: Heidi Rheim et al. GA4GH: International policies and standards for data sharing across genomic research and healthcare. (2021): Cell Genomics, Volume 1 Issue 2.
Siloing genomic data in institutions/jurisdictions limits learning and knowledge
GA4GH policy frameworks enable responsible genomic data sharing
GA4GH technical standards ensure interoperability, broad access, and global benefits
Data sharing across research and healthcare will extend the potential of genomics
Summary
The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.
In order for genomic and personalized medicine to come to fruition it is imperative that data siloes around the world are broken down, allowing the international collaboration for the collection, storage, transferring, accessing and analying of molecular and health-related data.
We had talked on this site in numerous articles about the problems data siloes produce. By data siloes we are meaning that collection and storage of not only DATA but intellectual thought are being held behind physical, electronic, and intellectual walls and inacessible to other scientisits not belonging either to a particular institituion or even a collaborative network.
Standardization and harmonization of data is key to this effort to sharing electronic records. The EU has taken bold action in this matter. The following section is about the General Data Protection Regulation of the EU and can be found at the following link:
The data protection package adopted in May 2016 aims at making Europe fit for the digital age. More than 90% of Europeans say they want the same data protection rights across the EU and regardless of where their data is processed.
The General Data Protection Regulation (GDPR)
Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data. This text includes the corrigendum published in the OJEU of 23 May 2018.
The regulation is an essential step to strengthen individuals’ fundamental rights in the digital age and facilitate business by clarifying rules for companies and public bodies in the digital single market. A single law will also do away with the current fragmentation in different national systems and unnecessary administrative burdens.
Directive (EU) 2016/680 on the protection of natural persons regarding processing of personal data connected with criminal offences or the execution of criminal penalties, and on the free movement of such data.
The directive protects citizens’ fundamental right to data protection whenever personal data is used by criminal law enforcement authorities for law enforcement purposes. It will in particular ensure that the personal data of victims, witnesses, and suspects of crime are duly protected and will facilitate cross-border cooperation in the fight against crime and terrorism.
The directive entered into force on 5 May 2016 and EU countries had to transpose it into their national law by 6 May 2018.
The following paper by the organiztion The Global Alliance for Genomics and Health discusses these types of collaborative efforts to break down data silos in personalized medicine. This organization has over 2000 subscribers in over 90 countries encompassing over 60 organizations.
Enabling responsible genomic data sharing for the benefit of human health
The Global Alliance for Genomics and Health (GA4GH) is a policy-framing and technical standards-setting organization, seeking to enable responsible genomic data sharing within a human rights framework.
he Global Alliance for Genomics and Health (GA4GH) is an international, nonprofit alliance formed in 2013 to accelerate the potential of research and medicine to advance human health. Bringing together 600+ leading organizations working in healthcare, research, patient advocacy, life science, and information technology, the GA4GH community is working together to create frameworks and standards to enable the responsible, voluntary, and secure sharing of genomic and health-related data. All of our work builds upon the Framework for Responsible Sharing of Genomic and Health-Related Data.
GA4GH Connect is a five-year strategic plan that aims to drive uptake of standards and frameworks for genomic data sharing within the research and healthcare communities in order to enable responsible sharing of clinical-grade genomic data by 2022. GA4GH Connect links our Work Streams with Driver Projects—real-world genomic data initiatives that help guide our development efforts and pilot our tools.
The Global Alliance for Genomics and Health (GA4GH) is a worldwide alliance of genomics researchers, data scientists, healthcare practitioners, and other stakeholders. We are collaborating to establish policy frameworks and technical standards for responsible, international sharing of genomic and other molecular data as well as related health data. Founded in 2013,3 the GA4GH community now consists of more than 1,000 individuals across more than 90 countries working together to enable broad sharing that transcends the boundaries of any single institution or country (see https://www.ga4gh.org).In this perspective, we present the strategic goals of GA4GH and detail current strategies and operational approaches to enable responsible sharing of clinical and genomic data, through both harmonized data aggregation and federated approaches, to advance genomic medicine and research. We describe technical and policy development activities of the eight GA4GH Work Streams and implementation activities across 24 real-world genomic data initiatives (“Driver Projects”). We review how GA4GH is addressing the major areas in which genomics is currently deployed including rare disease, common disease, cancer, and infectious disease. Finally, we describe differences between genomic sequence data that are generated for research versus healthcare purposes, and define strategies for meeting the unique challenges of responsibly enabling access to data acquired in the clinical setting.
GA4GH organization
GA4GH has partnered with 24 real-world genomic data initiatives (Driver Projects) to ensure its standards are fit for purpose and driven by real-world needs. Driver Projects make a commitment to help guide GA4GH development efforts and pilot GA4GH standards (see Table 2). Each Driver Project is expected to dedicate at least two full-time equivalents to GA4GH standards development, which takes place in the context of GA4GH Work Streams (see Figure 1). Work Streams are the key production teams of GA4GH, tackling challenges in eight distinct areas across the data life cycle (see Box 1). Work Streams consist of experts from their respective sub-disciplines and include membership from Driver Projects as well as hundreds of other organizations across the international genomics and health community.
Figure 1Matrix structure of the Global Alliance for Genomics and HealthShow full caption
Box 1GA4GH Work Stream focus areasThe GA4GH Work Streams are the key production teams of the organization. Each tackles a specific area in the data life cycle, as described below (URLs listed in the web resources).
(1)Data use & researcher identities: Develops ontologies and data models to streamline global access to datasets generated in any country9,10
(2)Genomic knowledge standards: Develops specifications and data models for exchanging genomic variant observations and knowledge18
(3)Cloud: Develops federated analysis approaches to support the statistical rigor needed to learn from large datasets
(4)Data privacy & security: Develops guidelines and recommendations to ensure identifiable genomic and phenotypic data remain appropriately secure without sacrificing their analytic potential
(5)Regulatory & ethics: Develops policies and recommendations for ensuring individual-level data are interoperable with existing norms and follow core ethical principles
(6)Discovery: Develops data models and APIs to make data findable, accessible, interoperable, and reusable (FAIR)
(7)Clinical & phenotypic data capture & exchange: Develops data models to ensure genomic data is most impactful through rich metadata collected in a standardized way
(8)Large-scale genomics: Develops APIs and file formats to ensure harmonized technological platforms can support large-scale computing
For more articles on Open Access, Science 2.0, and Data Networks for Genomics on this Open Access Scientific Journal see:
The UK Biobank (UKBB) this week unveiled to scientists the entire genomes of 200,000 people who are part of a long-term British health study.
The trove of genomes, each linked to anonymized medical information, will allow biomedical scientists to scour the full 3 billion base pairs of human DNA for insights into the interplay of genes and health that could not be gleaned from partial sequences or scans of genome markers. “It is thrilling to see the release of this long-awaited resource,” says Stephen Glatt, a psychiatric geneticist at the State University of New York Upstate Medical University.
Other biobanks have also begun to compile vast numbers of whole genomes, 100,000 or more in some cases (see table, below). But UKBB stands out because it offers easy access to the genomic information, according to some of the more than 20,000 researchers in 90 countries who have signed up to use the data. “In terms of availability and data quality, [UKBB] surpasses all others,” says physician and statistician Omar Yaxmehen Bello-Chavolla of the National Institute for Geriatrics in Mexico City.
Enabling your vision to improve public health
Data drives discovery. We have curated a uniquely powerful biomedical database that can be accessed globally for public health research. Explore data from half a million UK Biobank participants to enable new discoveries to improve public health.
This UKBB biobank represents genomes collected from 500,000 middle-age and elderly participants for 2006 to 2010. The genomes are mostly of a European descent. Other large scale genome sequencing ventures like Iceland’s DECODE, which collected over 100,000 genomes, is now a subsidiary of Amgen, and mostly behind IP protection, not Open Access as this database represents.
UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database is regularly augmented with additional data and is globally accessible to approved researchers undertaking vital research into the most common and life-threatening diseases. It is a major contributor to the advancement of modern medicine and treatment and has enabled several scientific discoveries that improve human health.
A summary of some large scale genome sequencing projects are show in the table below:
Biobank
Completed Whole Genomes
Release Information
UK Biobank
200,000
300,000 more in early 2023
TransOmics for Precision Medicien
161,000
NIH requires project specific request
Million Veterans Program
125,000
Non-Veterans Affairs researchers get first access
100,000 Genomes Project
120,000
Researchers must join Genomics England collaboration
All of Us
90,000
NIH expects to release 2022
Other Related Articles on Genome Biobank Projects in this Open Access Online Scientific Journal Include the Following:
From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery
Curator: Stephen J. Williams, PhD
Marc W. Kirschner*
Department of Systems Biology Harvard Medical School
Boston, Massachusetts 02115
With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like species, arise by descent with modification, so in their earliest forms even the founders of great dynasties are only marginally different than their sister fields and species. It is only in retrospect that we can recognize the significant founding events. Before embarking on a definition of systems biology, it may be worth remembering that confusion and controversy surrounded the introduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retrospect molecular biology was new and different. It introduced both new subject matter and new technological approaches, in addition to a new style.
As a point of departure for systems biology, consider the quintessential experiment in the founding of molecular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonlethal mutation in these genes in a multicellular organism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiological. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.
That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been perfected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new continent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the simplistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organisms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combinations, something that is not assured in chemistry. It also downplays the significant regulatory features that involve interactions between gene products, their localization, binding, posttranslational modification, degradation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the conserved genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different phenotypes in different tissues of metazoan organisms. These circuits may have certain robustness, but more important they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes themselves. Among other things it loads the deck in evolutionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.
Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the phenotype. One aspect of systems biology is the development of techniques to examine broadly the level of protein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.
High-throughput biology has opened up another important area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and reproducible environment. The real world of ecology, evolution, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later extended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some geneticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and protein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quantitative effects, partially masked or accentuated by other genetic and environmental conditions. To understand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environmental variation.
Extracts and explants are relatively accessible to synthetic manipulation. Next there is the explicit reconstruction of circuits within cells or the deliberate modification of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of describing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, proteins, cells in tissues, and whole organisms in their environment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.
You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biological organization and processes in terms of the molecular constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the importance of defining a succession of physiological states in that process, and on evolutionary biology and ecology for the appreciation that all aspects of the organism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology generates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of systems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.
Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.
5.1.5. Large-Scale Proteomics
While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.
All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.
5.2. Genetic Approaches
Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.
Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.
5.2.1. Resistance Cloning
The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.
In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].
While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].
When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].
While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.
5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens
When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).
An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].
The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].
Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].
An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].
From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.
SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY
Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence
The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question
1. Introduction
The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.
Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)
Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).
Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16].
2. Systems Biology in Cancer Research
Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].
In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.
2.1. Biological Network Analysis for Biomarker Validation
The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].
2.2. De Novo Construction of Biological Networks
While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].
2.3. Network Based Machine Learning
A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].
In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.
3. Network-Based Learning in Cancer Research
As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.
3.1. Molecular Characterization with Network Information
Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].
Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.
3.2. Tumor Heterogeneity Study with Network Information
The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].
In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].
3.3. Drug Target Identification with Network Information
In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].
Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.
4. Deep Learning in Cancer Research
DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].
In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].
4.1. Challenges for Deep Learning in Cancer Research
Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].
Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).
4.2. Molecular Charactization with Network and DNN Model
DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].
Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].
4.3. Tumor Heterogeneity with Network and DNN Model
As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].
4.4. Drug Target Identification with Networks and DNN Models
In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].
The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].
4.5. Graph Neural Network Model
In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].
In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].
4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge
The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].
As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].
Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.
“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”
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Use of Systems Biology in Anti-Microbial Drug Development
Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965
In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.
Genome Sequences and Proteomic Structural Databases
In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.
Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.
There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.
We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018; Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.
FIGURE 2
Figure 2.(A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).
Examples of Understanding and Combatting Resistance
The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.
FIGURE 3
Figure 3.(A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).
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