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eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, CANCER BIOLOGY & Innovations in Cancer Therapy, Conference Coverage with Social Media, Intelligent Information Systems on March 12, 2019| Leave a Comment »

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

Announcement

Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME

@pharma_BI

@AVIVA1950

#KIsymposium

AGENDA

https://custom.cvent.com/C09E632203614BC295DA40B01F45218F/files/47a27352fc1f4eb486236a34203f6396.pdf

https://ki.mit.edu/news/symposium/2019

 

8:00–9:00 Coffee Break Registration

 

9:00 –9:10 Introductory Remarks Tyler Jacks Koch Institute, MIT Phillip Sharp Koch Institute, MIT

• INTERDISCIPLINARY research at the interface of Cancer with other disciplines, this year CS, ML,

9:10–10:30 Machine Learning in Cancer Research: The Need and the Opportunity

Moderator: Phillip Sharp, Nobel Laureate 

• ML and cancer @MIT over a decade under discussion, ML & Cancer in research is embryonic
• REENGINEERING Healthcare using ML: understand disease, treating it
• MIT Stephen A. Schwarzman College of Computing, AI and Healthcare – an MIT commitment of a $MILLION
• COST OF DRUGS IS A THIRD IN TREATMENT — TOO HIGH,

James P. Allison MD Anderson Cancer Center, Nobel Laureate FOR CURE OF CANCER, THE ONLY ONE

Immune checkpoint blockade in cancer therapy: Historical perspective, new opportunities, and prospects for cures

• long survival on ipilimumab – Lung cancer pleural effusion
• CTLA-4Blockade – attenuated or Terminate Proliferation vs unrestrained Proliferation – APC
• PD-1 – Ipi/Nivo vs. Ipi in metastatic Melanoma – 10 years survival after initial three years
• FDA Approved CTLA-4 and Anti-PD-1 for a dozen indication
• CD4, CD8, NKT – CHeckpoint blockage modulates MC38 infiltrating T cell population frequency
• Cellular mechanism interaction
• T cell differentiation is complex – negative costimulation regulate T cell differentiation
• comprehensive profiling of peripheral T cell arising
• Expansion of phynotype – distinct gene expression
• Transcription loss of PD-1 expands CD8 T cells
• Nuanced Model – T cell differentiation
• Role of T cells in Immunotherapy

Aviv Regev Broad Institute; Koch Institute, MIT

Cell atlases as roadmaps to cancer

• Experimental data and computation data
• Tumors: a complex cellular ecosystem
• Genetic pathology, Combination therapy Genetic variants and
• Components vs Programs (regulatory, pathways, tissue modules, disease road map)
• ML Applications: Immunotherapy in melanoma
• Learning an single cell profiling and spatial organization – massively parallel cell profiling
• malignant cells from T cell cold tumors
• T cell T cell CD8 Malignant: Resistant
• Computational search predicts CDK4/6 as program regulators
• the resistance program is reversed abemaciclib sensitized to B16
• Resistance to immunotherapy in Melanoma
• Generalize across types of cancers – Resistance to treatment
• Atlas for Synovial sarcoma has anti-tumor activity in its immune environment
• Melanoma vs Synovial sarcoma (SS18-SSX fusion directly controls the core oncogenic program
• Target the core oncogenic program therapeutically
• IFN from CD8 T NF repress the program
• Insi2Vec: Define a cell by intrinsic and extrinsic and spatial features: generalize across patient samples in melanoma
• Identifying key patio-molecular archetypes
• Expanding to full Transcriptome
• Emerging toolbox for spatial genomics: Protein, RNA,
• Compressed sensing,

Regina Barzilay MIT Computer Science and Artificial Intelligence Laboratory; Koch Institute,

MIT How machine learning changes cancer research

• ML & Clinical Oncology
• Data Science perspective – Raw Data to Cure: Extract information
• NLP – penetration of deepLearning @MGH
• Drug discovery – New molecules
• MIT – ML in drug discovery
• Risk models: age, family history, prior breast procedures breast density
• High risk and Breast density
• Myth #1: DEMOCRATIZATION OF AI – deep learning and customization
• Myth @2: Bias – Deep Learning allows across population studies
• Myth #3: Black-Box Architecture 0 @MIT Interpretable models
• J-Clinic @MIT – ML and Healthcare Hospitals are recruited to use the algorithms

11:00–12:25 Machine Learning to Decipher Cellular and Molecular Mechanisms in Cancer

Moderator: Matthew Vander

Heiden Michael Angelo Stanford University

Comprehensive enumeration of immune cells in solid tumors using Multiplexed ion beam imaging (MIBI)

• Tissue imaging – high dimensional, multiplexing: high sensitivity, dynamic range,
• GI Tissue selection – Immunofluorescence (IF)  fluorophores – elemental reporters and mass spectrometry  Label antibody,
• Immune populations in triple negative breast cancer
• Patient stratification survival
• mixed tumor: Immune cells, non immune cells – response to PD-1
• Single cell: imaging data trained by nuclear interior ( nuclei only, , segmentation channel using DeepCell v1.0
• TB granulomas,
• Cell type identification with deep learning – No downstream
• 3D Segmentation Melanoma
• Assays and Ion sources

Olga Troyanskaya Princeton University

Decoding cancer genomes with deep learning

• genome to phenotype – BIG genomics data to understand human disease
• exosome – non coding region
• gene regulation affected by genome for gene expression
• sequence specific
• Interpreting a genome: predicting the effect of any mutation: coding variant vs non-coding variance – regulatory code, transcription marks, DeepSEA – model learns from one genome interactions of biochemical level models, variants motives cell type
• Interpreting a Genome: predicting the effect of any mutation: Noncoding variant effect in ASD from WGS data
• DeNovo mutations: How impactful are these mutations in Autism patients – to % not explained by sequencing
• DeepSEA identifies significant noncoding regulatory mutation burden in ASD
• Cell-type specific expression models capture tissue-specific
• ExPecto-prioritized putative casual GWAS variants
• HumanBase Networks best model selected are

Dana Pe’er Sloan Kettering Institute

Manifolds underlying plasticity in development, regeneration and cancer

• Asynchronous Nature of Cells
• Single cell transitional states, homeostatic
• Representing population structure as a neighbor graph: each node is a cell connected to a neighborhood – diffusion components Magic: Using structure for data recovery
• PALANTIR: a probabilistic model for cell fate
• Learn differentiation trajectories/pseudo-time ordering
• Computing Differentiation probabilities stochastic process – branch
• The gut tube comprises cells of two distinct regions
• mammal cell plasticity – endodermal organ identities in space
• VE cells express key organ lineage regulators similarly to DE cell
• Uncovering unappreciated lineage relationship
• Lung adenocarcinoma – tumor – 10% are cancer cell the rest are not therefore – they are ORGANS
• Primary tumor exhibit epithelial progenitor cells
• Pancreatic Ductal Adenocarcinoma – ductal Metaplasia
• Immune ecosystem reprogrammed
• Leptomeningeal niche — iron binding – adopt environment and prevent iron from the immune system
• Tumor plasticity – Mutation Kras + injury to cause pancreatic cancer

 

Peter Sorger Harvard Medical School

Measuring and modeling variability in drug response in cells, tissues and clinical trials

• combination therapy and drug synergy
• Re-digitalization of 300 clinical trials
• combination immunotherapy: Assumption each patient benefits from both = synergy
• vs the assumption of NO interaction: each Patient benefit from ONE or the Other
• Novartis PDX models
• Gastric Cancer PDXs
• Three way combination drug therapy – a deeper look at RAF and MEK inhibitors in BRAF mutant melanoma
• Pharmacology of ‘reconstituted pulses” independent action
• Heterogeneous response – sophisticated stratification of patients Four biopsy vs Three Biopsy
• Highly multiplexed, high resolution image of FFPE specimens
• Assaying the effects of multiple possible
• Cancer Precision Medicine per NCI
• Precision medicine based on therapeutic response at sisngel cell level
• Pharmacological synergy is over emphsized – time sensitivity prevent the interactions expected

12:30–2:00 Lunch Break

2:00–3:00 Big Data, Computation and the Future of Health Care

Moderator: Susan Hockfield

Jay Bradner Novartis Institutes for BioMedical Research

• AI is a Tool in all phases of drug discovery integrated analysis of epigenetics
• strategy for investment in 400 Scientists, big data
• monitoring of Clinical Trial around the  World
• generative chemistry – 100 years of discovery in chemistry
• validation models – bring trials to patients – distributed model of participation
• catalyst – ML in biological experimentation
• Published 700 articles per year at Novartis by 400 scientists
• Population Data sets
• IN 5 YEARS MASSIVE data will dominate molecule selection in Drug delivery

Aine Hanly VP Amgen

• 17% improvement in survival rate in 50 years
• Data infrastructure investment paid off
• Partnerships, interoperability in EMR

Clifford Hudis American Society of Clinical Oncology (ASCO)

• 75% of communication is by FAX still
• White cell data count – convert data structure on EMR
• Data in the Cloud for sharing
• 165 Million in US — employer covers health cost
• ACCESS to 2,000 clinical trials for repurposing

Constance Lehman Massachusetts General Hospital

• Pathway – Screening mammograms is run via the algorithm, report generated, Radiologist accept or reject the mobile presented
• more domains need AI and specialties of Medicine
• Development and implementation then dissemination to multiple sites
• In 1000 mammogram only 8 cancers detected

David Schenkein GOOGLE Venture (GV)

• Regeneron, Geisinger Health System, Amgen – Big data repositories are built
• Clinical Trials and big data
• delivery side: AI: Pathology and Radiology, early diagnostics:
• AI in MDs Offices to record the entire sessions
• diagnostics – Foundation Medicine diagnostics had difficult time to prove improvement to justify reimbursement

 

3:00–3:15 Break

 

3:15–4:40 Machine Learning into the Clinic

 

Moderator: Sangeeta Bhatia, Koch Institute, MIT

Tommi S. Jaakkola MIT Computer Science and Artificial Intelligence Laboratory

Machine learning for drug design

• Automation arises from data
• How much data is enough? assay size vs all spectrum
• MIT Consortium on Drug Discovery: 13 Pharma, chemistry Department, Chemical Engineering Department, CSMIT Consortium on Pharmaceutical Discovery
• multi-resolution representations
• substructures with precision attachment – what atoms are underlined
• predicting molecular properties
• programmatic optimization – machine translation problem in analogy – learn the mapping principles from examples
• Example comparisons – Programmatic optimization – Success %
• Understanding: Accuracy- interpretable, statistical constraints inserted
• integrations with biology and cancer research
• Automating Drug Design – learn from exampl

Andrew Beck PathAI

Artificial intelligence for pathology: From discovery to AI-powered companion diagnostics

• Pathologic diagnosis informs all subsequent medical decisions
• Discordance among pathologists is common in interpretation of breast biopsy
• Discordance among pathologists is common in interpretation of skin biopsy – melanoma
• Discordance among pathologists is common in interpretation across all fields: NSCLC, bladder,
• AI – deep learning is driving major advances in computer vision
• Reduce errors training set vs validation set
• computer vision vs Human eye – machine learning is BETTER
• Labeled and counting data standardization requires work with the community of Pathologists
• Automatic interpretation of cells and tissues
• Immuno-oncology
• TisSue and cellular phenologies – Pathological phenotypes
• BMS – CD8 continuum – Phenotype : Identify stromal and parenchymal CD8 infalmmation
• AI project with Novartis

Brian Wolpin Dana-Farber Cancer Institute

Early detection of pancreatic cancer

• ML for Pancreatic Cancer Early detection
• Metabolic alterations: Muscle wasting, subcutaneous adipose
• Automated Peripheral Tissue Segmentation
• 30,000 scans: changes by race, age, : Image-Based Risk Modeling within Large Health Systems

longitudinal Cohorts: Risk Factors

• Diabetes related to pancreatic cancer
• risk for weight and Diabetes
• Medication: Start and Stopped: NSAIDs, Tylenol, H2 Blocker, PPI, anti- HTN,
• ML – early detection: altered systemic metabolism

Stephen Friend University of Oxford

From complex to complicated problems: What are the known unknowns, and unknown unknowns in using wearables to forecast symptom transitions

• Pattern recognition for prognosis NEJM 20 years took since Breast cancer started to benefit from pattern recognition
• Participants-Centered
• 10M smart watches
• APPLE – many studies Smart watch – 15,000 participants donate data via their watch – large scale ALERT based on data analysis
• Inter-individual Diversity
• Personal Health Assistant
• all day sensing & recording
• Health Assessment – signals to Symptoms
• Deep Learning – multiple stages of non-linear feature transformation
• Vector Institute – data analysis was done [Pregnancy]
• Forecasting – binary labels: Well or Sick, intra and inter-individual longitudinal
• 4YouandMe  – Foundation
• Wearables for Cancer patients

4:40–4:45 Closing Remarks Phillip Sharp Koch Institute, MIT

 

 

Machine Learning and Cancer The 18th Annual Koch Institute Summer Symposium on June 14, 2019 at MIT’s Kresge Auditorium will focus on Machine Learning and Cancer. Both fields are undergoing dramatic changes, and their integration holds great promise for cancer research, diagnostics, and therapeutics. Cancer treatment and research have advanced rapidly with an increasing reliance on data-driven decisions. The volume, complexity, and diversity of research and clinical data—from genomics and single-cell molecular and image-based profiles to histopathology, clinical imaging, and medical records—far surpasses the capacity of individual scientists and physicians. However, they offer a remarkable opportunity to new approaches for data science and machine learning to provide holistic and intelligible interpretations to trained experts and patients alike. These advances will make it possible to provide far better diagnostics, discover possible chemical pathways for de novo synthesis of therapeutic compounds, predict accurately the risk of individuals for development of specific cancers years before metastatic spread, and determine the combination of agents that will stimulate immune rejection of a tumor or selectively induce the death of all cells in a tumor. The symposium will address these issues through three sessions: Machine Learning in Cancer Research: the Need and the Opportunity Machine Learning to Decipher Cellular and Molecular Mechanisms in Cancer Machine Learning into the Clinic Sessions will be followed by a panel discussion of broadly informed experts moderated by MIT President Emerita Susan Hockfield. Introductory remarks will be given by symposium co-chairs and Koch Institute faculty members Regina Barzilay, Aviv Regev and Phillip Sharp.   Keynote Speakers | Machine Learning in Cancer Research: the Need and the Opportunity James P. Allison, PhD MD Anderson Cancer Center Regina Barzilay, PhD MIT Computer Science and Artificial Intelligence Lab, Koch Institute for Integrative Cancer Research at MIT Aviv Regev, PhD Broad Institute, Koch Institute for Integrative Cancer Research at MIT   Session Speakers Michael R. Angelo, MD, PhD Stanford Unviersity Andrew Beck PathAI Stephen H. Friend, MD, PhD Sage Bionetworks Tommi Jaakkola, PhD MIT Computer Science and Artificial Intelligence Lab Dana Pe’er, PhD Memorial Sloan Kettering Cancer Center Peter Sorger, PhD Harvard Medical School Olga Troyanskaya, PhD Princeton University Brian Wolpin, MD Dana-Farber Cancer Institute   Panel Discussion | Big Data, Computation and the Future of Health Care James (Jay) Bradner, MD Novartis Clifford A. Hudis, MD American Society of Clinical Oncology Constance D. Lehman, MD, PhD Massachusetts General Hospital Norman (Ned) Sharpless, MD National Cancer Institute   Moderator: Susan Hockfield, PhD Koch Institute for Integrative Cancer Research at MIT [if gte mso 9]>

SOURCE

From: 2019 Koch Institute Symposium <ki-events@mit.edu>

Reply-To: <ki-events@mit.edu>

Date: Tuesday, March 12, 2019 at 11:30 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Invitation to the 2019 Koch Institute Symposium – Machine Learning and Cancer

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Role of Informatics in Precision Medicine: Can It Drive the Next Cost Efficiencies in Oncology Care?

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer - General, Cancer and Current Therapeutics, Cancer Informatics, Computational Biology/Systems and Bioinformatics, Health Economics and Outcomes Research, HealthCare IT, Intelligent Information Systems, interventional oncology, Patient-centered Medicine, Personalized Medicine Coalition, Precision Cancer Medicine, Uncategorized, tagged Big data, Cancer Care, cancer cost, cancer database, cancer outcomes, cancer patient management, Health Information Technology, healthcare costs, healthcare innovation, healthcare IT, informatics, innovation in healthcare, Machine Learning, value based medicine on January 3, 2019| Leave a Comment »

Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

 

Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”.  The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.

 

Below is the mp4 video and audio for this webinar.  Notes on each of the slides with a few representative slides are also given below:

Please click below for the mp4 of the webinar:

 

 

• worldwide oncology related care to increase by 40% in 2020
• big movement to participatory care: moving decision making to the patient. Need for information
• cost components focused on clinical action
• use informatics before clinical stage might add value to cost chain

 

 

 

 

Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making

 

 

 

1.   Needs of Clinicians

– informatic needs for clinical enrollment

– informatic needs for obtaining drug access/newer therapies

2.  Needs of C-suite/health system executives

– informatic needs to help focus of quality of care

– informatic needs to determine health outcomes/metrics

3.  Needs of Payers

– informatic needs to determine quality metrics and managing costs

– informatics needs to form guidelines

– informatics needs to determine if biomarkers are used consistently and properly

– population level data analytics

 

 

 

 

 

 

 

 

 

 

 

 

What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.

• innovations in data depth and breadth
• need to aggregate information to inform intervention

Different players in value chains have different data needs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Depth: Cumulative Understanding of disease

Data Depth: Cumulative number of oncology transactions

• technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
• NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
• data depth more important in clinical setting as it drives solutions and cost effective interventions.  For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
• technologies are moving to data support
• evidence will need to be tied to umbrella value propositions
• Informatic solutions will have to prove outcome benefit

 

 

 

 

 

How will Machine Learning be involved in the healthcare value chain?

• increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
• Interoperability of DATABASES Important!  Many Players in this space don’t understand the complexities integrating these datasets

Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Broad Institute launches Merkin Institute for Transformative Technologies in Healthcare

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Paradoxical Findings in HealthCare Delivery and Outcomes: Economics in MEDICINE – Original Research by Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at HMS

Google & Digital Healthcare Technology

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Live Conference Coverage @Medcity Converge 2018 Philadelphia: Oncology Value Based Care and Patient Management

2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data

The Need for an Informatics Solution in Translational Medicine

 

 

 

 

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Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Big Data, BioBanking, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biomarkers & Medical Diagnostics, Computational Biology/Systems and Bioinformatics, Health Economics and Outcomes Research, Intelligent Information Systems, Population Health Management, Genetics & Pharmaceutical, tagged #mobilehealth, Artificial intelligence, biomarker, blockchain, clinical genomics, computational algorithms, economics, healthcare, healthcare delivery, healthcare reimbursement, Innovation, medical information, medical IT, omic data, patient control, patient data on December 10, 2018| Leave a Comment »

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

1. Recent achievements in next-generation artificial intelligence
2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
3. Open source blockchain Exonium and its application for healthcare marketplace
4. A blockchain-based platform allowing patients to have control of their data and manage access
5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

• Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
• Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:

1. Prediction of drug properties(2, 3) and toxicities(4)
2. Biomarker development (5)
3. Cancer diagnosis (6)
4. First FDA approved system based on deep learning Arterys Cardio DL

• Other promising systems of deep learning include:
  • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
  • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
  • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
  • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

• Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
• Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
• Blockchain uses a consensus algorithm even though end users may have their own privacy key
• Applied cryptography measures and routines are used to decentralize authentication (publicly available)
• Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

https://soundcloud.com/vitalhealth/real-world-data-pay-for-performance-or-blockchain-richard-bergstrom-is-always-ahead-of-the-curve

References

1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

STEWART ROGERS@THEREALSJR AUGUST 3, 2018 8:30 AM

From VentureBeat.com

Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

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Bioinformatics Tool Review: Genome Variant Analysis Tools

Posted in Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer Informatics, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Genome Biology, Personalized and Precision Medicine & Genomic Research, Uncategorized, tagged bioinformatic tools, Bioinformatics, Bioinformatics methodologies, disease variants, DNA, Ensembl, European Bioinformatics Institute, genetic variants, National Center for Biotechnology Information, NCBI, next gen sequencing (NGS), NGS tests, SNP, Whole genome sequencing on October 23, 2018| Leave a Comment »

Bioinformatics Tool Review: Genome Variant Analysis Tools

Curator: Stephen J. Williams, Ph.D.

Updated 02/07/2021

Updated 11/15/2018

The following post will be an ongoing curation of reviews of gene variant bioinformatic software.

The Ensembl Variant Effect Predictor.

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F.

Genome Biol. 2016 Jun 6;17(1):122. doi: 10.1186/s13059-016-0974-4.

Author information

1

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.

2

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.

3

European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.

Abstract

The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.

Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015).  However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here).  To this effect major sequencing projects have been initiated worldwide including:

• Iceland
• UK (100,000 Genome Project)
• USA
• Genome 10K

A more thorough curation of sequencing projects can be seen in the following post:

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein.  Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.

To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.

Summary of Features

• Annotation: VEP can annotate two broad categories of genomic variants
  • Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
  • Larger structural variants > 50 nucleotides
• Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
• Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
• Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
• Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation.  Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
• Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants.  Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation.  Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
• Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats.  Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
• Choice of user interface
  • Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
  • VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub.  The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
  • VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.

 

Watch Video on VES Instructional Webinar: https://youtu.be/7Fs7MHfXjWk

Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP

Availability of data and materials

The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.

• Project name: Ensembl Variant Effect Predictor
• Project home page: http://www.ensembl.org/vep
• Archived version: https://github.com/Ensembl/ensembl-tools/archive/release/83.zip
• Zenodo deposit: https://zenodo.org/record/50492#.Vx9TJ5MrKEI
• Operating system: platform independent
• Programming language: Perl
• Other requirements: Perl 5.10 or higher and the DBI and DBD::mysql modules
• License: Apache 2.0
• Any restrictions to use by non-academics: none.

References

Large-scale discovery of novel genetic causes of developmental disorders.

Deciphering Developmental Disorders Study.

Nature. 2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962

Updated 11/15/2018

Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools

Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:

• For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
• As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
• For rare diseases, pathogenic allele frequencies are generally low

Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.

In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene.  These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007).  It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).

References

Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.

Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.

Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.

Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.

Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.

Frequently used bioinformatics tools overestimate the damaging effect of allelic variants.

Andersen LL¹, Terczyńska-Dyla E¹, Mørk N², Scavenius C¹, Enghild JJ¹, Höning K³, Hornung V^3,4, Christiansen M^5,6, Mogensen TH^2,6, Hartmann R⁷.

Abstract

We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.

Methodology

1. Identification of rare variants
2. Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
3. Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
4. In-silico predictions: Variants classified by the in silico variant pathogenicity prediction programs: SIFT, Mutation Assessor, FATHMM, PROVEAN, SNAP2, PolyPhen2, PhD-SNP, SNP&GO, FATHMM-MKL, MutationTaster2, PredictSNP, Condel, MetaSNP, and CADD. Each program returned prediction scores measuring likelihood of a variant either being ‘deleterious’ or ‘neutral’. Prediction accuracy measured as

ACC = (true positive+true negative)/(true positive+true negative+false positive+false negative)

1. Validation of prediction software/tools

In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function.  Western blot was performed to confirm the expression of the constructs.

Results

Table 2 Summary of the bioinformatic predictions	HSE variants						IFNLR1 variants						Overall ACC
	TN	TP	FN	FP	Total	ACC	TN	TP	FN	FP	Total	ACC
Uniform cutoff
SIFT	4	1	0	4	9	0.56	8	1	0	7	16	0.56	0.56
Mutation assessor	6	1	0	2	9	0.78	9	1	0	6	16	0.63	0.68
FATHMM	7	1	0	1	9	0.89							0.89
PROVEAN	8	1	0	0	9	1.00	11	1	0	4	16	0.75	0.84
SNAP2	5	1	0	3	9	0.67	8	0	1	7	16	0.50	0.56
PolyPhen2	6	1	0	2	9	0.78	12	1	0	3	16	0.81	0.80
PhD-SNP	7	1	0	1	9	0.89	11	1	0	4	16	0.75	0.80
SNPs&GO	8	1	0	0	9	1.00	14	1	0	1	16	0.94	0.96
FATHMM MKL	4	1	0	4	9	0.56	13	0	1	2	16	0.81	0.72
MutationTaster2	4	0	1	4	9	0.44	14	0	1	1	16	0.88	0.72
PredictSNP	6	1	0	2	9	0.78	11	1	0	4	16	0.75	0.76
Condel	6	1	0	2	9	0.78							0.78
Meta-SNP	8	1	0	0	9	1.00	11	1	0	4	16	0.75	0.84
CADD	2	1	0	6	9	0.33	8	0	1	7	16	0.50	0.44
MSC 95% cutoff
SIFT	5	1	0	3	9	0.67	8	1	0	8	16	0.50	0.56
PolyPhen2	6	1	0	2	9	0.78	13	1	0	3	16	0.81	0.80
CADD	4	1	0	4	9	0.56	7	0	1	9	16	0.44	0.48

Note: TN: true negative, TP: true positive, FN: false negative, FP: false positive, ACC: accuracy

Functional testing (data obtained from reporter construct experiments) were considered as the correct outcome.

Three prediction tools (PROVEAN, SNP&GO, and MetaSNP correctly predicted the effect of all nine variants tested.

Updated 02/07/2021

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution

Jun-Ichi Takeda , Kentaro Nanatsue , Ryosuke Yamagishi , Mikako Ito , Nobuhiko Haga ², Hiromi Hirata , Tomoo Ogi , Kinji Ohno in NAR Genomics and  Bioinformatics. 2020 May 26;2(2):lqaa038.doi: 10.1093/nargab/lqaa038. eCollection 2020 Jun.

• PMID: 33543123
• PMCID: PMC7671370
• DOI: 10.1093/nargab/lqaa03

Abstract

In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision-recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).

Source: https://pubmed.ncbi.nlm.nih.gov/33543123/

ADDRESS: A database of disease-associated human variants incorporating protein structure and folding stabilities

Jaie Woodard , Chengxin Zhang , Yang Zhang in J Mol Biol. 2021 Feb 1;166840. doi: 10.1016/j.jmb.2021.166840.

• PMID: 33539887
• DOI: 10.1016/j.jmb.2021.166840

Abstract

Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.

Source: https://pubmed.ncbi.nlm.nih.gov/33539887/

PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Sebastian Niehus , Hákon Jónsson , Janina Schönberger  , Eythór Björnsson, Doruk Beyter , Hannes P Eggertsson , Patrick Sulem  Kári Stefánsson , Bjarni V Halldórsson Birte Kehr 

in Nature Communications. 2021 Feb 1;12(1):730.doi: 10.1038/s41467-020-20850-5.

• PMID: 33526789
• DOI: 10.1038/s41467-020-20850-5

Abstract

Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

Source: https://pubmed.ncbi.nlm.nih.gov/33526789/

Other articles related to Genomics and Bioinformatics on this online Open Access Journal Include:

Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

Large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes

US Personalized Cancer Genome Sequencing Market Outlook 2018 –

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

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Live Conference Coverage @Medcitynews Converge 2018 Philadelphia: Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Posted in Alzheimer's Disease, Big Data, Big Data & Analytics, BioBanking, Biomarkers & Medical Diagnostics, Cancer and Current Therapeutics, Cancer Informatics, Cancer Screening, Diagnostics and Lab Tests, Meta-analysis of transcriptome data, Pharmaceutical Industry Competitive Intelligence, Pharmacotherapy and Cell Activity, Scientific & Biotech Conferences: Press Coverage, Transcriptomics, Uncategorized, tagged AD biomarkers, cancer biomarkers, clinical trial 2.0, innovations in cancer detection, multiple schlerosis, oncoproteomics, Predictive Cancer Biomarkers, Proteomics, Transcriptomics on July 11, 2018| Leave a Comment »

5:00 – 5:45 PM Early Diagnosis Through Predictive Biomarkers, NonInvasive Testing

Reporter: Stephen J. Williams, Ph.D.

 

Diagnosing cancer early is often the difference between survival and death. Hear from experts regarding the new and emerging technologies that form the next generation of cancer diagnostics.

Moderator: Heather Rose, Director of Licensing, Thomas Jefferson University
Speakers:
Bonnie Anderson, Chairman and CEO, Veracyte @BonnieAndDx
Kevin Hrusovsky, Founder and Chairman, Powering Precision Health @KevinHrusovsky

Bonnie Anderson and Veracyte produces genomic tests for thyroid and other cancer diagnosis.  Kevin Hrusovksy and Precision Health uses peer reviewed evidence based medicine to affect precision medicine decision.

Bonnie: aim to get a truth of diagnosis.  Getting tumor tissue is paramount as well as properly preserved tissue.  They use deep RNA sequencing  and machine learning  in their clinically approved tests.

Kevin: Serial biospace entrepreneur.  Two diseases, cancer and neurologic, have been diseases which have been hardest to get reproducible and validated biomarkers of early disease.  He concentrates on protein biomarkers.

Heather:  FDA has recently approved drugs for early disease intervention.  However the use of biomarkers can go beyond patient stratification in clinical trials.

Kevin: 15 approved drugs for MS but the markers are scans looking for brain atrophy which is too late of an endpoint.  So we need biomarkers of early disease progression.  We can use those early biomarkers of disease progression so pharma can target those early biomarkers and or use those early biomarkers of disease progression  for endpoint

Bonnie: exciting time in the early diagnostics field. She prefers transcriptomics to DNA based methods such as WES or WGS (whole exome or whole genome sequencing).  It was critical to show data on the cost savings imparted by their transcriptomic based thryoid cancer diagnostic test for payers to consider this test eligible for reimbursement.

Kevin: There has been 20 million  CAT scans for  cancer but it is estimated 90% of these scans led to misdiagnosis. Biomarker  development  has revolutionized diagnostics in this disease area.  They have developed a breakthrough panel of ten protein biomarkers in serum which he estimates may replace 5 million mammograms.

All panelists agreed on the importance of regulatory compliance and the focus of new research should be on early detection.  In addition they believe that Dr. Gotlieb’s appointment to the FDA is a positive for the biomarker development field, as Dr. Gotlieb understands the potential and importance of early detection and prevention of disease.  Kevin also felt Dr. Gotlieb understands the importance of incorporating biomarkers as endpoints in clinical trials.  Over 750 phase 1,2, and 3 clinical trials use biomarker endpoints but the pharma companies still need to prove the biomarkers clinical relevance to the FDA.They also agreed it would be helpful to involve advocacy groups in putting more pressure on the healthcare providers and policy makers on this importance of diagnostics as a preventative measure.

In addition, the discovery and use of biomarkers as disease endpoints has led to a resurgence of Alzheimer’s disease drug development by companies which have previously given up on these type of neurodegenerative diseases.

Kevin feels proteomics offers great advantages over DNA-based diagnostics, especially in cancer such as ovarian cancer, where a high degree of specificity for a diagnostic test is required to ascertain if a woman should undergo prophylactic oophorectomy.  He suggests that a new blood-based protein biomarker panel is being developed for early detection of some forms of ovarian cancer.

Please follow on Twitter using the following #hash tags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

@medcitynews

 

Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

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Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, BioIT: BioInformatics, Intelligent Information Systems on May 10, 2018| Leave a Comment »

Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)

 

Reporter: Aviva Lev-Ari, PhD, RN

 

BOSTON – May 10, 2018 – Vyasa Analytics, a provider of deep learning software and analytics for life sciences and healthcare organizations, today announces three pre-built deep learning analytics modules for its Cortex software at Bio-IT World Conference & Expo. Cortex enables the secure, scalable application of deep learning-based artificial intelligence (AI) analytics to enterprise data, identifying patterns, relationships and concepts across disparate data sources.

 

The new Neural Concept Recognition, Image Analytics and ChemVector analytics modules in Cortex enable life sciences organizations to quickly and easily apply deep learning analytics to large data streams of text, images and chemical structures. Like all deep learning analytical modules in Cortex’s library, these new modules allow users to ask complex questions of their data and use the answers to gain critical insights.

 

“Life sciences and healthcare organizations are using AI tools to advance research and development and deliver better patient care. Deep learning algorithms provide a set of powerful approaches that help us apply analytics more effectively and comprehensively across large scale data sources,” said Dr. Christopher Bouton, founder and CEO of Vyasa. “The idea of AI has been around for decades, but we are now experiencing a perfect storm of GPU-based computing power, deep learning algorithm advances and highly scalable data sources that enables paradigm-shifting machine learning and analytics capabilities.”

 

Vyasa will be demoing three deep learning analytics modules for Cortex at Bio-IT World 2018 in Boston from May 15 to 17, including:

 

• Neural Concept Recognition. This module can be trained on text concepts (e.g. drugs, diseases, pathways, conditions, side effects, genes) in structured and unstructured data. Users can ask Cortex complex questions across large scale data sets, and discover unexpected relationships between concept types. Concept recognition analytics is applicable to a wide range of use cases from competitive intelligence, to drug repurposing and EHR analytics.

 

• Life Sciences R&D Specialized Image Analytics. Deep learning enables novel, powerful forms of image analytics, capable of being trained to detect patterns and objects in large scale image data sources. With just a few clicks in Cortex, the user can connect large streams of image data and apply analytics to those sources. Vyasa has finely-tuned this analysis for life sciences images, and it is ideal for cell assay screening, drug manufacturing and post-market screening for counterfeit packaging and tablets.
• ChemVector de novo Compound Design. This proprietary Cortex module applies deep learning to chemical structures. Users can drag and drop one or more SDF files containing SMILES strings into Cortex, and Cortex can identify and generate novel compounds that optimize critical variables such as log-p, molecular weight and synthetic viability. ChemVector can be used with a range of other chemistry-specific analytical modules also available in Cortex.

 

 

Dr. Bouton, Vyasa’s founder and CEO, received his BA in Neuroscience (Magna Cum Laude) from Amherst College in 1996 and his Ph.D. in Molecular Neurobiology from Johns Hopkins University in 2001. Previously Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.

 

Visit Vyasa and demo Cortex at booth #632, and watch the explainer video at www.vyasa.com.

About Vyasa Analytics

Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations. Cortex is Vyasa’s secure, highly scalable software platform for collaborative knowledge discovery and data analytics. Using Vyasa’s proprietary Neural Concept Recognition technology, Cortex identifies trends and patterns across disparate data sources, empowering project teams to gain insights and drive better decision making. Learn more at www.vyasa.com.

 

 

	Angela Zmyslinski Account Executive azmyslinski@matternow.com Office – 401-330-2800

SOURCE

From: Angela Zmyslinski <azmyslinski@matternow.com>

Date: Thursday, May 10, 2018 at 2:39 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: RE: Demo deep learning software for life sciences at Bio-IT World 2018

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