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Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Posted in Advanced Computing Platform, AI, Big Data, BioBanking, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer and Current Therapeutics, CAST - Alternative to CRISPR/Cas9, Computational Biology/Systems and Bioinformatics, CRISPR/Cas9 & Gene Editing, Diagnostic Immunology, Digital HealthCare – biotech & internet joint ventures, Disease Biology, DNA repair, Drug Delivery Platform Technology, Gene Editing Impact on Longevity, Genetics & Innovations in Treatment, Genome Biology, Genomic Testing: Methodology for Diagnosis, Health Care System by Country, Health Economics and Outcomes Research, HealthCare IT, Human Immune System in Health and in Disease, Immuno-Oncology & Genomics, Immunodiagnostics, Liquid Biopsy: Circulating Tumor Cells in Urine and Blood, Microfuidics, Single Cell Genomics, Variation in human protein-coding regions on December 9, 2019| Leave a Comment »

Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Reporter: Aviva Lev-Ari, PhD, RN

This report is based on an article in Nature Medicine | VOL 25 | December 2019 | 1800–1809 | http://www.nature.com/naturemedicine

Looking forward 25 years: the future of medicine.

Nat Med 25, 1804–1807 (2019) doi:10.1038/s41591-019-0693-y

Aviv Regev, PhD

Core member and chair of the faculty, Broad Institute of MIT and Harvard; director, Klarman Cell Observatory, Broad Institute of MIT and Harvard; professor of biology, MIT; investigator, Howard Hughes Medical Institute; founding co-chair, Human Cell Atlas.

millions of genome variants, tens of thousands of disease-associated genes, thousands of cell types and an almost unimaginable number of ways they can combine, we had to approximate a best starting point—choose one target, guess the cell, simplify the experiment.
In 2020, advances in polygenic risk scores, in understanding the cell and modules of action of genes through genome-wide association studies (GWAS), and in predicting the impact of combinations of interventions.
we need algorithms to make better computational predictions of experiments we have never performed in the lab or in clinical trials.
Human Cell Atlas and the International Common Disease Alliance—and in new experimental platforms: data platforms and algorithms. But we also need a broader ecosystem of partnerships in medicine that engages interaction between clinical experts and mathematicians, computer scientists and engineers

Feng Zhang, PhD

investigator, Howard Hughes Medical Institute; core member, Broad Institute of MIT and Harvard; James and Patricia Poitras Professor of Neuroscience, McGovern Institute for Brain Research, MIT.

fundamental shift in medicine away from treating symptoms of disease and toward treating disease at its genetic roots.
Gene therapy with clinical feasibility, improved delivery methods and the development of robust molecular technologies for gene editing in human cells, affordable genome sequencing has accelerated our ability to identify the genetic causes of disease.
1,000 clinical trials testing gene therapies are ongoing, and the pace of clinical development is likely to accelerate.
refine molecular technologies for gene editing, to push our understanding of gene function in health and disease forward, and to engage with all members of society

Elizabeth Jaffee, PhD

Dana and Albert “Cubby” Broccoli Professor of Oncology, Johns Hopkins School of Medicine; deputy director, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins.

a single blood test could inform individuals of the diseases they are at risk of (diabetes, cancer, heart disease, etc.) and that safe interventions will be available.
developing cancer vaccines. Vaccines targeting the causative agents of cervical and hepatocellular cancers have already proven to be effective. With these technologies and the wealth of data that will become available as precision medicine becomes more routine, new discoveries identifying the earliest genetic and inflammatory changes occurring within a cell as it transitions into a pre-cancer can be expected. With these discoveries, the opportunities to develop vaccine approaches preventing cancers development will grow.

Jeremy Farrar, OBE FRCP FRS FMedSci

Director, Wellcome Trust.

shape how the culture of research will develop over the next 25 years, a culture that cares more about what is achieved than how it is achieved.
building a creative, inclusive and open research culture will unleash greater discoveries with greater impact.

John Nkengasong, PhD

Director, Africa Centres for Disease Control and Prevention.

To meet its health challenges by 2050, the continent will have to be innovative in order to leapfrog toward solutions in public health.
Precision medicine will need to take center stage in a new public health order— whereby a more precise and targeted approach to screening, diagnosis, treatment and, potentially, cure is based on each patient’s unique genetic and biologic make-up.

Eric Topol, MD

Executive vice-president, Scripps Research Institute; founder and director, Scripps Research Translational Institute.

In 2045, a planetary health infrastructure based on deep, longitudinal, multimodal human data, ideally collected from and accessible to as many as possible of the 9+ billion people projected to then inhabit the Earth.
enhanced capabilities to perform functions that are not feasible now.
AI machines’ ability to ingest and process biomedical text at scale—such as the corpus of the up-to-date medical literature—will be used routinely by physicians and patients.
the concept of a learning health system will be redefined by AI.

Linda Partridge, PhD

Professor, Max Planck Institute for Biology of Ageing.

Geroprotective drugs, which target the underlying molecular mechanisms of ageing, are coming over the scientific and clinical horizons, and may help to prevent the most intractable age-related disease, dementia.

Trevor Mundel, MD

President of Global Health, Bill & Melinda Gates Foundation.

finding new ways to share clinical data that are as open as possible and as closed as necessary.
moving beyond drug donations toward a new era of corporate social responsibility that encourages biotechnology and pharmaceutical companies to offer their best minds and their most promising platforms.
working with governments and multilateral organizations much earlier in the product life cycle to finance the introduction of new interventions and to ensure the sustainable development of the health systems that will deliver them.
deliver on the promise of global health equity.

Josep Tabernero, MD, PhD

Vall d’Hebron Institute of Oncology (VHIO); president, European Society for Medical Oncology (2018–2019).

genomic-driven analysis will continue to broaden the impact of personalized medicine in healthcare globally.
Precision medicine will continue to deliver its new paradigm in cancer care and reach more patients.
Immunotherapy will deliver on its promise to dismantle cancer’s armory across tumor types.
AI will help guide the development of individually matched
genetic patient screenings
the promise of liquid biopsy policing of disease?

Pardis Sabeti, PhD

Professor, Harvard University & Harvard T.H. Chan School of Public Health and Broad Institute of MIT and Harvard; investigator, Howard Hughes Medical Institute.

the development and integration of tools into an early-warning system embedded into healthcare systems around the world could revolutionize infectious disease detection and response.
But this will only happen with a commitment from the global community.

Els Toreele, PhD

Executive director, Médecins Sans Frontières Access Campaign

we need a paradigm shift such that medicines are no longer lucrative market commodities but are global public health goods—available to all those who need them.
This will require members of the scientific community to go beyond their role as researchers and actively engage in R&D policy reform mandating health research in the public interest and ensuring that the results of their work benefit many more people.
The global research community can lead the way toward public-interest driven health innovation, by undertaking collaborative open science and piloting not-for-profit R&D strategies that positively impact people’s lives globally.

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Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

Posted in Advanced Computing Platform, Big Data, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biological Networks, Biological Networks, Gene Regulation and Evolution, Computational Biology/Systems and Bioinformatics, Gene Regulation, Gene Regulation and Evolution, Genome Biology, Intelligent Information Systems, Single Cell Genomics, Single-cell sequencing, Variation in human protein-coding regions on December 8, 2019| Leave a Comment »

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

Curator & Reporter: Aviva Lev-Ari, PhD, RN

Subjects:

The Scientific Frontier is presented in Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

Abstract

How transcription factors (TFs) interpret cis-regulatory DNA sequence to control gene expression remains unclear, largely because past studies using native and engineered sequences had insufficient scale. Here, we measure the expression output of >100 million synthetic yeast promoter sequences that are fully random. These sequences yield diverse, reproducible expression levels that can be explained by their chance inclusion of functional TF binding sites. We use machine learning to build interpretable models of transcriptional regulation that predict ~94% of the expression driven from independent test promoters and ~89% of the expression driven from native yeast promoter fragments. These models allow us to characterize each TF’s specificity, activity and interactions with chromatin. TF activity depends on binding-site strand, position, DNA helical face and chromatin context. Notably, expression level is influenced by weak regulatory interactions, which confound designed-sequence studies. Our analyses show that massive-throughput assays of fully random DNA can provide the big data necessary to develop complex, predictive models of gene regulation.

The Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis is presented in the following Table

50	Liu, X., Li, Y. I. & Pritchard, J. K. Trans effects on gene expression can drive omnigenic inheritance. Cell 177, 1022–1034 e1026 (2019).
5	Muerdter, F. et al. Resolving systematic errors in widely used enhancer activity assays in human cells. Nat. Methods 15, 141–149 (2018).
6	Wang, X. et al. High-resolution genome-wide functional dissection of transcriptional regulatory regions and nucleotides in human. Nat. Commun. 9, 5380 (2018).
15	Yona, A. H., Alm, E. J. & Gore, J. Random sequences rapidly evolve into de novo promoters. Nat. Commun. 9, 1530 (2018).
4	van Arensbergen, J. et al. Genome-wide mapping of autonomous promoter activity in human cells. Nat. Biotechnol. 35, 145–153 (2017).
14	Cuperus, J. T. et al. Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences. Genome Res. 27, 2015–2024 (2017).
31	Levo, M. et al. Systematic investigation of transcription factor activity in the context of chromatin using massively parallel binding and expression assays. Mol. Cell 65, 604–617 e606 (2017).
49	Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
54	de Boer, C. High-efficiency S. cerevisiae lithium acetate transformation. protocols.io https://doi.org/10.17504/protocols.io.j4tcqwn (2017).
59	Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous systems. arXiv 1603.04467 (2016).
20	Shalem, O. et al. Systematic dissection of the sequence determinants of gene 3’ end mediated expression control. PLoS Genet. 11, e1005147 (2015).
55	Deng, C., Daley, T. & Smith, A. D. Applications of species accumulation curves in large-scale biological data analysis. Quant. Biol. 3, 135–144 (2015).
9	Hughes, T. R. & de Boer, C. G. Mapping yeast transcriptional networks. Genetics 195, 9–36 (2013).
10	Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).
19	Kosuri, S. et al. Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc. Natl Acad. Sci. USA 110, 14024–14029 (2013).
7	Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).
18	de Boer, C. G. & Hughes, T. R. YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities. Nucleic Acids Res. 40, D169–D179 (2012).
56	Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
61	Cherry, J. M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).
11	Nutiu, R. et al. Direct measurement of DNA affinity landscapes on a high-throughput sequencing instrument. Nat. Biotechnol. 29, 659–664 (2011).
26	Zhang, Z. et al. A packing mechanism for nucleosome organization reconstituted across a eukaryotic genome. Science 332, 977–980 (2011).
30	Ganapathi, M. et al. Extensive role of the general regulatory factors, Abf1 and Rap1, in determining genome-wide chromatin structure in budding yeast. Nucleic Acids Res. 39, 2032–2044 (2011).
52	Erb, I. & van Nimwegen, E. Transcription factor binding site positioning in yeast: proximal promoter motifs characterize TATA-less promoters. PloS One 6, e24279 (2011).
3	Kinney, J. B., Murugan, A., Callan, C. G. Jr. & Cox, E. C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA107, 9158–9163 (2010).
8	Gertz, J., Siggia, E. D. & Cohen, B. A. Analysis of combinatorial cis-regulation in synthetic and genomic promoters. Nature 457, 215–218 (2009).
16	Wunderlich, Z. & Mirny, L. A. Different gene regulation strategies revealed by analysis of binding motifs. Trends Genet. 25, 434–440 (2009).
27	Hesselberth, J. R. et al. Global mapping of protein–DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).
29	Hartley, P. D. & Madhani, H. D. Mechanisms that specify promoter nucleosome location and identity. Cell 137, 445–458 (2009).
51	Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).
58	Segal, E. & Widom, J. From DNA sequence to transcriptional behaviour: a quantitative approach. Nat. Rev. Genet. 10, 443–456 (2009).
2	Yuan, Y., Guo, L., Shen, L. & Liu, J. S. Predicting gene expression from sequence: a reexamination. PLoS Comput. Biol. 3, e243 (2007).
46	Hibbs, M. A. et al. Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics 23, 2692–2699 (2007).
25	Liu, X., Lee, C. K., Granek, J. A., Clarke, N. D. & Lieb, J. D. Whole-genome comparison of Leu3 binding in vitro and in vivo reveals the importance of nucleosome occupancy in target site selection. Genome Res. 16, 1517–1528 (2006).
34	Roberts, G. G. & Hudson, A. P. Transcriptome profiling of Saccharomyces cerevisiae during a transition from fermentative to glycerol-based respiratory growth reveals extensive metabolic and structural remodeling. Mol. Genet. Genomics 276, 170–186 (2006).
48	Tanay, A. Extensive low-affinity transcriptional interactions in the yeast genome. Gen. Res. 16, 962–972 (2006).
53	Tong, A. H. & Boone, C. Synthetic genetic array analysis in Saccharomyces cerevisiae. Methods Mol. Biol. 313, 171–192 (2006).
57	Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
62	Chua, G. et al. Identifying transcription factor functions and targets by phenotypic activation. Proc. Natl Acad. Sci. USA 103, 12045–12050 (2006).
17	Arnosti, D. N. & Kulkarni, M. M. Transcriptional enhancers: intelligent enhanceosomes or flexible billboards? J. Cell. Biochem. 94, 890–898 (2005).
21	Granek, J. A. & Clarke, N. D. Explicit equilibrium modeling of transcription-factor binding and gene regulation. Genome Biol. 6, R87 (2005).
1	Beer, M. A. & Tavazoie, S. Predicting gene expression from sequence. Cell 117, 185–198 (2004).
28	Bernstein, B. E., Liu, C. L., Humphrey, E. L., Perlstein, E. O. & Schreiber, S. L. Global nucleosome occupancy in yeast. Genome Biol. 5, R62 (2004).
44	Kim, T. S., Kim, H. Y., Yoon, J. H. & Kang, H. S. Recruitment of the Swi/Snf complex by Ste12-Tec1 promotes Flo8-Mss11-mediated activation of STA1 expression. Mol. Cell. Biol. 24, 9542–9556 (2004).
45	Harbison, C. T. et al. Transcriptional regulatory code of a eukaryotic genome. Nature 431, 99–104 (2004).
60	Kent, N. A., Eibert, S. M. & Mellor, J. Cbf1p is required for chromatin remodeling at promoter-proximal CACGTG motifs in yeast. J. Biol. Chem. 279, 27116–27123 (2004).
22	Kulkarni, M. M. & Arnosti, D. N. Information display by transcriptional enhancers. Development 130, 6569–6575 (2003).
24	Conlon, E. M., Liu, X. S., Lieb, J. D. & Liu, J. S. Integrating regulatory motif discovery and genome-wide expression analysis. Proc. Natl Acad. Sci. USA 100, 3339–3344 (2003).
43	Neely, K. E., Hassan, A. H., Brown, C. E., Howe, L. & Workman, J. L. Transcription activator interactions with multiple SWI/SNF subunits. Mol. Cell. Biol. 22, 1615–1625 (2002).
23	Bussemaker, H. J., Li, H. & Siggia, E. D. Regulatory element detection using correlation with expression. Nat. Genet. 27, 167–171 (2001).
37	Haurie, V. et al. The transcriptional activator Cat8p provides a major contribution to the reprogramming of carbon metabolism during the diauxic shift in Saccharomyces cerevisiae. J. Biol. Chem. 276, 76–85 (2001).
39	Grauslund, M. & Ronnow, B. Carbon source-dependent transcriptional regulation of the mitochondrial glycerol-3-phosphate dehydrogenase gene, GUT2, from Saccharomyces cerevisiae. Can. J. Microbiol. 46, 1096–1100 (2000).
42	Cullen, P. J. & Sprague, G. F. Jr. Glucose depletion causes haploid invasive growth in yeast. Proc. Natl Acad. Sci. USA 97, 13619–13624 (2000).
38	Sato, T. et al. TheE-box DNA binding protein Sgc1p suppresses the gcr2 mutation, which is involved in transcriptional activation of glycolytic genes in Saccharomyces cerevisiae. FEBS Lett. 463, 307–311 (1999).
40	Madhani, H. D. & Fink, G. R. Combinatorial control required for the specificity of yeast MAPK signaling. Science 275, 1314–1317 (1997).
41	Gavrias, V., Andrianopoulos, A., Gimeno, C. J. & Timberlake, W. E. Saccharomyces cerevisiae TEC1 is required for pseudohyphal growth. Mol. Microbiol. 19, 1255–1263 (1996).
36	Hedges, D., Proft, M. & Entian, K. D. CAT8, a new zinc cluster-encoding gene necessary for derepression of gluconeogenic enzymes in the yeast Saccharomyces cerevisiae. Mol. Cell. Biol. 15, 1915–1922 (1995).
47	Bednar, J. et al. Determination of DNA persistence length by cryo-electron microscopy. Separation of the static and dynamic contributions to the apparent persistence length of DNA. J. Mol. Biol. 254, 579–594 (1995).
32	Axelrod, J. D., Reagan, M. S. & Majors, J. GAL4 disrupts a repressing nucleosome during activation of GAL1 transcription in vivo. Genes Dev. 7, 857–869 (1993).
33	Morse, R. H. Nucleosome disruption by transcription factor binding in yeast. Science 262, 1563–1566 (1993).
12	Oliphant, A. R., Brandl, C. J. & Struhl, K. Defining the sequence specificity of DNA-binding proteins by selecting binding sites from random-sequence oligonucleotides: analysis of yeast GCN4 protein. Mol. Cell. Biol. 9, 2944–2949 (1989).
35	Forsburg, S. L. & Guarente, L. Identification and characterization of HAP4: a third component of the CCAAT-bound HAP2/HAP3 heteromer. Genes Dev. 3, 1166–1178 (1989).
13	Horwitz, M. S. & Loeb, L. A. Promoters selected from random DNA sequences. Proc. Natl Acad. Sci. USA 83, 7405–7409 (1986).

To access each reference as a live link, go to the number in the first column in the Table and look it up in the List of References in the Link, below

https://www.nature.com/articles/s41587-019-0315-8

Author information

Affiliations

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Carl G. de Boer
- , Eeshit Dhaval Vaishnav
- , Nir Friedman
- & Aviv Regev
Howard Hughes Medical Institute and Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Eeshit Dhaval Vaishnav
- & Aviv Regev
School of Computer Science and Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- Ronen Sadeh
- & Nir Friedman
Initiative for Maximizing Student Development Program, University of New Mexico, Albuquerque, NM, USA
- Esteban Luis Abeyta

Contributions

C.G.D. and A.R. drafted the manuscript, with all authors contributing. C.G.D. analyzed the data. C.G.D., E.D.V., E.L.A. and R.S. performed the experiments. A.R. and N.F. supervised the research.

Corresponding authors

Correspondence to Carl G. de Boer or Aviv Regev.

Ethics declarations

Competing interests

A.R. is an SAB member of Thermo Fisher Scientific, Neogene Therapeutics, Asimov, and Syros Pharmaceuticals, an equity holder of Immunitas, and a founder of and equity holder in Celsius Therapeutics. All other authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cite this article

Boer, C.G., Vaishnav, E.D., Sadeh, R. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat Biotechnol (2019) doi:10.1038/s41587-019-0315-8

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Received

24 January 2019
Accepted

16 October 2019
Published

02 December 2019
DOI

https://doi.org/10.1038/s41587-019-0315-8

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Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

Posted in Advanced Computing Platform, Artificial Intelligence - General, Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Intelligent Information Systems, LPBI Group Founder - Aviva Lev-Ari on December 2, 2019| Leave a Comment »

Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

Curator: Aviva Lev-Ari, PhD, RN

Based on Jesse Anderson’s work on data teams Kathleen Walch in Why Data Scientists Aren’t Data Engineers makes several keen distinctions between the two skill sets.

I can attest that she is absolutely correct. See below, a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

These key distinctions are:

Data Scientists vs Data Engineers

In the mid-2000s, we saw the emergence of the Data Scientist position. As cited in the O’Reilly article: “This increase in the demand for data scientists has been driven by the success of the major Internet companies. Google, Facebook, LinkedIn, and Amazon have all made their marks by using data creatively: not just warehousing data, but turning it into something of value.” Not surprisingly, any organization that has data of value is looking at data science and data scientists to increasingly extract more value from that information.

Originating from roots in statistical modeling and data analysis, data scientists have backgrounds in advanced math and statistics, advanced analytics, and increasingly machine learning / AI. The focus of data scientists is, unsurprisingly, data science — that is to say, how to extract useful information from a sea of data, and how to translate business and scientific informational needs into the language of information and math. Data scientists need to be masters of statistics, probability, mathematics, and algorithms that help to glean useful insights from huge piles of information. These data scientists usually have learned programming out of necessity more than anything else in order to run programs and run advanced analysis on data. As a result, the code that data scientists have usually been tasked to write, is of a minimal nature – only as necessary to accomplish a data science task (R is a common language for them to use) and work best when they are provided clean data to run advanced analytics on. A data scientist is a scientist who creates hypothesis, runs tests and analysis of the data, and then translates their results for someone else in the organization to easily view and understand.

On the other hand, data scientists can’t perform their jobs without access to large volumes of clean data. Extracting, cleaning, and moving data is not really the role of a data scientist, but rather that of a data engineer. Data Engineers have programming and technology expertise, and have previously been involved with data integration, middleware, analytics, business data portal, and extract-transform-load (ETL) operations. The data engineer’s center of gravity and skills are focused around big data and distributed systems, and experience with programming languages such as Java, Python, Scala, and scripting tools and techniques. Data engineers are challenged with the task of taking data from a wide range of systems in structured and unstructured formats, and data which is usually not “clean”, with missing fields, mismatched data types, and other data-related issues. These data engineers need to use their programming, integration, architecture, and systems skills to clean all the data and put it into a format and system that data scientists can then use to analyze, build their data models, and provide value to the organization. In this way, the role of a data engineer is an engineer who designs, builds and arranges data.

Can there be a combined Data Scientist-Engineer role?

While it might seem that the roles of a data scientist and data engineer are distinct, data scientists and data engineers share many traits and skill sets. These overlapping skills include the necessity to work with and manipulate big data sets, programming skills to apply operations to the data, data analytics skills, and general fluency with systems operations.

Rather than engineering and programming-centric tools, data scientists need data science-centric tools. Right now there’s a growing collection of these tools, often emerging from data or predictive analytics environments that suit the needs of data scientists. However, it’s possible that even more business-centric tools might be appropriate, especially as the data scientists become more embedded with the line of business. For example, decades ago if you wanted to operate on large volumes of data in a spreadsheet-like format, this involved programming, but tools like Excel introduced things like pivot tables and now business managers are able to perform all sorts of analyses. It’s only a matter of time before tools like Excel embed data science capabilities, or business-centric data mining and analysis tools into their products.

As the talent gap for data scientists continues to widen, there is no doubt that we will see new tools created out of necessity to allow non-technical (read: business) people to run, test, and analyze data. Strategic business managers will begin to learn data science, without needing or wanting programming or data integration experience. Traditional data scientists will still be needed to run very complex analysis of data. For the most part however, basic analysis will move more to the business unit due to increasingly easy-to-use tools. This means we have still yet to see which tool or technology will be the dominant one for ML and data science in the enterprise.

My SOURCES for the evolution of the field of Data Science are the following:

Jesse Anderson’s work on data teams

Learn How to Create and Manage Big Data Teams

This Free, 73 Page E-Book is the Complete Guide to Successful Big Data projects

I’m really tired of seeing Big Data projects fail. They fail for both technical and managerial reasons. They all fail for similar reasons and that’s just sad because we can fix or prevent them. Gartner’s research shows that 85% of Big Data projects don’t even make it into production.

“Only 15 percent of businesses reported deploying their big data project to production, effectively unchanged from last year (14 percent).”

October 4, 2016 Gartner Press Release

https://www.bigdatainstitute.io/data-engineering-teams-book/

December, 1, 2019, 9:48 am

Why Data Scientists Aren’t Data Engineers

Kathleen Walch

Managing Partner & Principal Analyst at AI Focused Research and Advisory firm Cognilytica

https://www.forbes.com/sites/cognitiveworld/2019/12/01/why-data-scientists-arent-data-engineers/amp/?__twitter_impression=true

Translating Between Computer Science and Statistics

Posted on December 1, 2019

Gil Press

https://whatsthebigdata.com/2019/12/01/translating-between-computer-science-and-statistics/

Jan 8, 2019, 06:18am

The AI Chronicles: Combining Statistical Analysis And Computing From Hollerith To Zuckerberg

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2019/01/08/the-ai-chronicles-combining-statistical-analysis-and-computing-from-hollerith-to-zuckerberg/#23cf507c73b3

Jan 2, 2015, 10:48am

A Very Short History Of The Internet And The Web

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2015/01/02/a-very-short-history-of-the-internet-and-the-web-2/#a45c9307a4e2

May 28, 2013, 09:09am

A Very Short History Of Data Science

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#1e7db3e155cf

May 9, 2013, 09:45am

A Very Short History Of Big Data

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/#16c2043b65a1

Apr 8, 2013, 09:16am

A Very Short History of Information Technology (IT)

Gil Press Contributor

Enterprise & Cloud

https://www.forbes.com/sites/gilpress/2013/04/08/a-very-short-history-of-information-technology-it/#3f5491022440

A Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals: Analytics Designer, Aviva Lev-Ari, PhD, RN

On this landscape about IT, The Internet, Analytics, Statistics, Big Data, Data Science and Artificial Intelligence, I am to tell stories on my own pioneering work in data science, Algorithm-based decision support systems design for different organizations in several sectors of the US economy:

Startups:

TimeØ Group
Concept Five Technologies, Inc.
MDSS, Inc.
LPBI Group

Top Tier Management Consulting: SRI International, Monitor Group;
OEM: Amdahl Corporation;
Top 6th System Integrator: Perot System Corporation;
FFRDC: MITRE Corporation.
Publishing industry: was Director of Research at McGraw-Hill/CTB.
Northeastern University, Researcher on Cardiovascular Pharmaco-therapy at Bouve College of Health Sciences (Independent research guided by Professor of Pharmacology)

Type of institutions:

For-Profit corporations: Amdahl Corp, PSC, McGraw-Hill
For-Profit Top Tier Consulting: Monitor Company, Now Deloitte
Not-for-Profit Top Tier Consulting: SRI International
FFRDC: MITRE
eScientific Publishing: LPBI Group: Developers of Curation methodology for e-Articles [N = 3,700], electronic Table of Contents for e-Books in Medicine [N = 16, https://lnkd.in/ekWGNqA] and e-Proceedings of Biotech Conferences [N = 70].

Autobiographical Annotations: Tribute to My Professors

Pioneering implementations of analytics to business decision making: contributions to domain knowledge conceptualization, research design, methodology development, data modeling and statistical data analysis: Aviva Lev-Ari, UCB, PhD’83; HUJI MA’76

https://pharmaceuticalintelligence.com/2018/05/28/pioneering-implementations-of-analytics-to-business-decision-making-contributions-to-domain-knowledge-conceptualization-research-design-methodology-development-data-modeling-and-statistical-data-a/

Recollections of Years at UC, Berkeley, Part 1 and Part 2

Recollections: Part 1 – My days at Berkeley, 9/1978 – 12/1983 – About my doctoral advisor, Allan Pred, other professors and other peers

https://pharmaceuticalintelligence.com/2018/03/15/recollections-my-days-at-berkeley-9-1978-12-1983-about-my-doctoral-advisor-allan-pred-other-professors-and-other-peer/

Recollections: Part 2 – “While Rolling” is preceded by “While Enrolling” Autobiographical Alumna Recollections of Berkeley – Aviva Lev-Ari, PhD’83

https://pharmaceuticalintelligence.com/2018/05/24/recollections-part-2-while-rolling-is-preceded-by-while-enrolling-autobiographical-alumna-recollections-of-berkeley-aviva-lev-ari-phd83/

Accomplishments

The Digital Age Gave Rise to New Definitions – New Benchmarks were born on the World Wide Web for the Intangible Asset of Firm’s Reputation: Pay a Premium for buying e-Reputation

For @AVIVA1950, Founder, LPBI Group @pharma_BI: Twitter Analytics [Engagement Rate, Link Clicks, Retweets, Likes, Replies] & Tweet Highlights [Tweets, Impressions, Profile Visits, Mentions, New Followers] https://analytics.twitter.com/user/AVIVA1950/tweets

Thriving at the Survival Calls during Careers in the Digital Age – An AGE like no Other, also known as, DIGITAL

Reflections on a Four-phase Career: Aviva Lev-Ari, PhD, RN, March 2018

Was prepared for publication in American Friends of the Hebrew University (AFHU), May 2018 Newsletter, Hebrew University’s HUJI Alumni Spotlight Section.

Aviva Lev-Ari’s profile was up on 5/3/2018 on AFHU website under the Alumni Spotlight at https://www.afhu.org/

On 5/11/2018, Excerpts were Published in AFHU e-news.

https://us10.campaign-archive.com/?u=5c25136c60d4dfc4d3bb36eee&id=757c5c3aae&e=d09d2b8d72

https://www.afhu.org/2018/05/03/aviva-lev-ari/

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scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Posted in Advanced Computing Platform, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data & Analytics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biotechnology, Cancer Genomics, Cardiovascular Pharmacogenomics, Cell Biology, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Data Science, Genome Biology, Genomic Analysis of EKG Electrophysiology Genomics, Genomic Endocrinology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, Immuno-Oncology & Genomics, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, RNA Biology, RNA Biology, Cancer and Therapeutics, Single Cell Genomics, Single-cell sequencing, Uncategorized, tagged comparative analysis, Computational Biology/Systems and Bioinformatics, computational genomics, scPopCorn, single-cell RNA sequencing, subpopulation on July 16, 2019| Leave a Comment »

scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

References:

https://www.sciencedirect.com/science/article/pii/S2405471219301887

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

https://ieeexplore.ieee.org/abstract/document/4031383

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

https://www.sciencedirect.com/science/article/pii/S2405471216302666

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Seven Alternative Designs to Quantum Computing Platform – The Race by IBM, Google, Microsoft, and Others

Posted in Advanced Computing Platform, Artificial Intelligence - Breakthroughs in Theories and Technologies, Big Data, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Intelligent Information Systems on May 27, 2019| Leave a Comment »

Seven Alternative Designs to Quantum Computing Platform – The Race by IBM, Google, Microsoft, and Others

Reporter: Aviva Lev-Ari, PhD, RN

Business Bets on a Quantum Leap

Quantum computing could help companies address problems as huge as supply chains and climate change. Here’s how IBM, Google, Microsoft, and others are racing to bring the tech from theory to practice.

By Robert Hackett

May 21, 2019

quantum computer at IonQ, an Alphabet-backed startup

A version of this article appears in the June 2019 issue of Fortune with the headline “The Race for Quantum Domination.”

Medicine

One day, your health may depend on a quantum leap.

Pharmaceutical giant Biogen teamed up with consultancy Accenture and startup 1QBit on a quantum computing experiment in 2017 aimed at molecular modeling, one of the more complex disciplines in medicine. The goal: finding candidate drugs to treat neurodegenerative diseases.

Microsoft is collaborating with Case Western Reserve University to improve the accuracy of MRI machines, which help detect cancer, using so-called quantum-inspired algorithms.

7 ways to win the quantum race

There are multiple ways that quantum computing could work.

Here’s a guide to which companies are backing which tech.

Superconducting uses an electrical current, flowing through special semiconductor chips cooled to near absolute zero, to produce computational “qubits.” Google, IBM, and Intel are pursuing this approach, which has so far been the front-runner.

Ion trap relies on charged atoms that are manipulated by lasers in a vacuum, which helps to reduce noisy interference that can contribute to errors. Industrial giant Honeywell is betting on this technique. So is IonQ, a startup with backing from Alphabet.

Neutral Atom Similar to the ion-trap method, except it uses, you guessed it, neutral atoms. Physicist Mikhail Lukin’s lab at Harvard is a pioneer.

Annealing designed to find the lowest-energy (and therefore speediest) solutions to math problems. Canadian firm D-Wave has sold multimillion-dollar machines based on the idea to Google and NASA. They’re fast, but skeptics question whether they qualify as “quantum.”

Silicon spin uses single electrons trapped in transistors. Intel is hedging its bets between the more mature superconducting qubits and this younger, equally semiconductor-friendly method.

Topological uses exotic, highly stable quasi-particles called “anyons.” Microsoft deems this unproven moonshot as the best candidate in the long run, though the company has yet to produce a single one.

Photonics uses light particles sent through special silicon chips. The particles interact with one another very little (good), but can scatter and disappear (bad). Three-year-old stealth startup Psi Quantum is tinkering away on this idea.

SOURCE

http://fortune.com/longform/business-quantum-computing/

www.worldmedicalinnovation.org

https://pharmaceuticalintelligence.com/2019/04/09/live-day-two-world-medical-innovation-forum-artificial-intelligence-boston-ma-usa-monday-april-9-2019/

Research and Development (R&D) Expenditure by Country represent time, capital, and effort being put into researching and designing the products of the future – Data from the UNESCO Institute for Statistics adjusted for purchasing-power parity (PPP).

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/05/26/research-and-development-rd-expenditure-by-country-represent-time-capital-and-effort-being-put-into-researching-and-designing-the-products-of-the-future-data-from-the-unesco-institute-for-s/

Resources on Artificial Intelligence in Health Care and in Medicine: Articles of Note at PharmaceuticalIntelligence.com @AVIVA1950 @pharma_BI

https://www.linkedin.com/pulse/resources-artificial-intelligence-health-care-note-lev-ari-phd-rn/

IBM’s Watson Health division – How will the Future look like?I

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2019/04/24/ibms-watson-health-division-how-will-the-future-look-like/

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Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com

Archive for the ‘Advanced Computing Platform’ Category

Medicine in 2045 – Perspectives by World Thought Leaders in the Life Sciences & Medicine

Looking forward 25 years: the future of medicine.

Aviv Regev, PhD

Feng Zhang, PhD

Elizabeth Jaffee, PhD

Jeremy Farrar, OBE FRCP FRS FMedSci

John Nkengasong, PhD

Eric Topol, MD

Linda Partridge, PhD

Trevor Mundel, MD

Josep Tabernero, MD, PhD

Pardis Sabeti, PhD

Els Toreele, PhD

Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis

The Scientific Frontier is presented in Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

Abstract

The Evolution of the Human Cell Genome Biology Field of Gene Expression, Gene Regulation, Gene Regulatory Networks and Application of Machine Learning Algorithms in Large-Scale Biological Data Analysis is presented in the following Table

To access each reference as a live link, go to the number in the first column in the Table and look it up in the List of References in the Link, below

Author information

Affiliations

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Howard Hughes Medical Institute and Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

School of Computer Science and Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel

Initiative for Maximizing Student Development Program, University of New Mexico, Albuquerque, NM, USA

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Data Science & Analytics: What do Data Scientists Do in 2020 and a Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals

My SOURCES for the evolution of the field of Data Science are the following:

Learn How to Create and Manage Big Data Teams

This Free, 73 Page E-Book is the Complete Guide to Successful Big Data projects

A Pioneer Practitioner’s Portfolio of Algorithm-based Decision Support Systems for Operations Management in Several Industrial Verticals: Analytics Designer, Aviva Lev-Ari, PhD, RN

Recollections of Years at UC, Berkeley, Part 1 and Part 2

scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Seven Alternative Designs to Quantum Computing Platform – The Race by IBM, Google, Microsoft, and Others

Business Bets on a Quantum Leap

7 ways to win the quantum race

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