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

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.

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

Jesse Anderson’s work on data teams

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:

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

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

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

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

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

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/

Advancing Cancer Research with Deep Learning Image Analysis

MinneBOS 2019 Field Guide to Data Science & Emerging Tech in the Boston Community, August 22, 2019 8AM to 5PM at Boston University Questrom School of Business, 595 Commonwealth Avenue, Boston, MA

Posted in Big Data, BioIT: BioInformatics, Conference Coverage with Social Media, Intelligent Information Systems on July 31, 2019| Leave a Comment »

MinneBOS 2019, Field Guide to Data Science & Emerging Tech in the Boston Community

August 22, 2019, 8AM to 5PM at Boston University Questrom School of Business, 595 Commonwealth Avenue, Boston, MA

MinneBOS – Boston’s Field Guide to Data Science & Emerging Tech

https://minnebos.sched.com/list/descriptions/

Announcement

Leaders in Pharmaceutical Business Intelligence (LPBI) Group

REAL TIME Press Coverage for

http://pharmaceuticalintelligence.com

Aviva Lev-Ari, PhD, RN

Director & Founder, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Editor-in-Chief, Open Access Online Scientific Journal, http://pharmaceuticalintelligence.com

Editor-in-Chief, BioMed e-Series, 16 Volumes in Medicine, https://pharmaceuticalintelligence.com/biomed-e-books/

@pharma_BI

@AVIVA1950

#MinneBos

Logo, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Our BioMed e-series

WE ARE ON AMAZON.COM

ekWGNqA

UPDATED AGENDA

Histopathological images are the gold standard tool for cancer diagnosis, whose interpretation requires manual inspection by expert pathologists. This process is time-consuming for the patients and subject to human error. Recent advances in deep learning models, particularly convolutional neural networks, combined with big databases of patient histopathology images will pave the path for cancer researchers to create more accurate guiding tools for pathologists. In this talk, I will review the latest advances of big data in healthcare analytics and focus on deep learning applications in cancer research. Targeted at a general audience, I will provide a high-level overview of technical concepts in deep learning image analysis, and describe a typical cloud-based workflow for tackling such big data problems. I will conclude my talk by sharing some of our most recent results based on a wide range of cancer types.

Speakers

Mohammad Soltanieh-ha, PhD

Clinical Assistant Professor, Boston University – Questrom

Mohammad is a faculty at Boston University, Questrom School of Business, where he teaches data analytics and big data to master’s students. Mohammad’s current research area involves deep learning and its applications in cancer research.

10:15am

Deep learning image recognition and classification models for fashion items

10:30am

Large scale image recognition and classification is an interesting and challenging problem. This case study uses fashion-MNIST dataset that involves 60000 training images and 10000 testing images. Several popular deep learning models are explored in this study to arrive at a suitable model with high accuracy. Although convolutional neural networks have emerged as a gold-standard for image recognition and classification problems due to speed and accuracy advantages, arriving at an optimal model and making several choices at the time of specifying model architecture, is still a challenging task. This case study provides the best practices and interesting insights.

Speakers

Bharatendra Rai

Professor, UMass Dartmouth

Bharatendra Rai, Ph.D. is Professor of Business Analytics in the Charlton College of Business at UMass Dartmouth. His research interests include machine learning & deep learning applications.

Train data: 60,000
Test data: 10,000
Dataset available from Google MNIST Fashion Data – items in DB: data already labelled
Label and Description
Architecture: Input >> Conv >> Conv >> Pooling >> Dropout << Dense <<Flatten << Dropout >> Output
CNN vs Fully connected: 320 parameters: 3x3x1x32 + [32 BIAS TERM] = 320 vs
fully connected network parameters is 16 million
Train the model: 15 iterations – Training and Validation
Actual vs Predicted: 94% was classified correctly = Accuracy: 94% 5974 vs 4700 (78%)
Confusion Matrix – Test 720 correctly classified for item 6 – Probability va Actual Vs Predicted
Image generation: Noise . gnerator Network > fake Image vs Real image – GAN Loss va Discriminator Loss
CNN network help reduce # of parameter
Droppot layers can help reduce overfitting
validation split of x%chooses last x% of train data
Generation of new data is challenging

11:00am

Health and Healthcare Data Visualization – See how you’re doing

11:15am

Rapid Data Science

Most companies today require fast, traceable, and actionable answers to their data questions. This talk will present the structure of the data science process along with cutting edge developments in computing and data science technology (DST) with direct applications to real world problems (with a lot of pictures!). Everything from modeling to team building will be discussed, with clear business applications.

Speakers

Erez Kaminski

Leaders Global Operations Fellow, MIT

Erez has spent his career helping companies solve problems using data science. He is currently a graduate student in computer science and business at MIT. Previously, he worked in data science at Amgen Inc. and as a technologist at Wolfram Research.

12:00pm

Lunch

1:00pm

Health and healthcare organizations are swimming in data but few have the skills to show and see the story in their data using the best practices of data visualization. This presentation raises awareness about the research that inform these best practice and stories from the front of groups who are embracing them and re-imagining how they display their data and information. These groups include the NYC Dept of Health & Mental Hygiene, The Centers for Medicare and Medicaid (CMS), and leading medical centers and providers across the country.

Speakers

Katherine Rowell

Co-Founder & Principal, Health Data Viz

Katherine Rowell is a health, healthcare, and data visualization expert. She is Co-founder and Principal of HealthDataViz, a Boston firm that specializes in helping healthcare organizations organize, design and present visual displays of data to inform their decisions and stimulate… Read More →

dashboard for Hospital CEOs

1:45pm

2:00pm

AI in Healthcare

Benefits, challenges and impact of AI and Cybersecurity on medicine.

Speakers

Vinit Nijhawan

Lecturer, Boston University

Vinit Nijhawan is an Entrepreneur, Academic, and Board Member with a track record of success, including 4 startups in 20 years.

US: Spends the most on Health Care (HC) death per 100K people is the highest
Eric Topol – Diagnosis is not done correctly, AI will help with diagnosis
Diagnosis — AI will have the most impact; VIRAL infections are diagnosed as bacterial infections and get antibiotics for treatment
Image Classification my ML – decline below to human misclassification
Training Data sets – Big data
Algorithms getting better
Data Capture getting better – HC as well
Investment in HC is the greatest
SECURITY related to Implentable Medical Devices = security attacks – hacking and sending signal to implentable devices

2:45pm

Patient centric AI: Saving lives with ML driven hospital interventions

3:00pm

Empower

This presentation will cover the use of machine learning for maximizing the impact of a hospital readmissions intervention program. With machine learning, clinical care teams can identify and focus their intervention efforts on patients with the highest risk of readmission. The talk will go over the goals, logistics, and considerations for defining, implementing, and measuring our ML driven intervention program. While covering some technical details, this presentation will focus on the business implementation of advanced technology for helping people live healthier lives.

Speakers

Miguel Martinez

Data Scientist, Optum

Miguel Martinez is a Data Scientist at Optum Enterprise Analytics. Relied on as a tech lead in advancing AI healthcare initiatives, he is passionate about identifying and developing data science solutions for the benefit of organizations and people.

3:30pm

Using Ontologies to Power AI Systems

3:45pm

There’s a great deal of confusion about the role of a knowledge architecture in artificial intelligence projects. Some people don’t believe that any reference data is necessary. But in reality reference data is required- even if there is no metadata or architecture definitions outside defined externally for an AI algorithm, someone has made the decisions about architecture and classification within the program. However, this will not work for every organization because there are terms, workflows, product attributes, and organizing principles that are unique to the organization and that need to be defined for AI tools to work most effectively.

Speakers

Seth Earley

CEO, Earley Information Science

Seth Earley is a published author and public speaker about artificial intelligence and information architecture. He wrote “There’s no AI without IA” which has become an industry catchphrase used by a number of people including Ginny Rometty, the CEO of IBM.

Ontology, taxonomies, thesauri – conceptual relationships
Object-Oriented Programming and Information Architecture using AI is Old wine in new bottles

4:15pm

Networking Social in Atrium

TBA

Senior Leadership Panel: Future Directions of Analytics

This panel includes senior leaders from across industry, academia & government to discuss challenges they are tackling, needs they anticipate and goals they will achieve

Moderators

Bonnie Holub, PhD

Industry & Business Data Science, Teradata

Bonnie has a PhD in Artificial Intelligence and specializes in correlating disparate sets of Big Data for actionable results.

Posted in Artificial Intelligence - General, Big Data, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Conference Coverage with Social Media, CRISPR/Cas9 & Gene Editing, Disease Biology, Small Molecules in Development of Therapeutic Drugs, DNA repair, Epigenetics and Environmental Factors, Ethics and Leadership, FDA Regulatory Affairs, Genome Biology, Global Partnering & Biotech Investment, Health Care System by Country, Health Economics and Outcomes Research, Health Law & Patient Safety, Healthcare costs and reimbursement, United States on June 8, 2019| Leave a Comment »

eProceedings for BIO 2019 International Convention, June 3-6, 2019 Philadelphia Convention Center; Philadelphia PA, Real Time Coverage by Stephen J. Williams, PhD @StephenJWillia2

eProceedings for BIO 2019 International Convention, June 3-6, 2019 Philadelphia Convention Center; Philadelphia PA, Real Time Coverage by Stephen J. Williams, PhD @StephenJWillia2

CONFERENCE OVERVIEW

Real Time Coverage of BIO 2019 International Convention, June 3-6, 2019 Philadelphia Convention Center; Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/05/31/real-time-coverage-of-bio-international-convention-june-3-6-2019-philadelphia-convention-center-philadelphia-pa/

LECTURES & PANELS

Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence: Realizing Precision Medicine One Patient at a Time, 6/5/2019, Philadelphia PA

Reporter: Stephen J Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-machine-learning-and-artificial-intelligence-realizing-precision-medicine-one-patient-at-a-time/

Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges, 6/5/2019. Philadelphia PA

Reporter: Stephen J Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-genome-editing-and-regulatory-harmonization-progress-and-challenges/

Real Time Coverage @BIOConvention #BIO2019: Precision Medicine Beyond Oncology June 5, 2019, Philadelphia PA

Reporter: Stephen J Williams PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-precision-medicine-beyond-oncology-june-5-philadelphia-pa/

Real Time @BIOConvention #BIO2019:#Bitcoin Your Data! From Trusted Pharma Silos to Trustless Community-Owned Blockchain-Based Precision Medicine Data Trials, 6/5/2019, Philadelphia PA

Reporter: Stephen J Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-bioconvention-bio2019bitcoin-your-data-from-trusted-pharma-silos-to-trustless-community-owned-blockchain-based-precision-medicine-data-trials/

Real Time Coverage @BIOConvention #BIO2019: Keynote Address Jamie Dimon CEO @jpmorgan June 5, 2019, Philadelphia, PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-keynote-address-jamie-dimon-ceo-jpmorgan-june-5-philadelphia/

Real Time Coverage @BIOConvention #BIO2019: Chat with @FDA Commissioner, & Challenges in Biotech & Gene Therapy June 4, 2019, Philadelphia, PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-chat-with-fda-commissioner-challenges-in-biotech-gene-therapy-june-4-philadelphia/

Falling in Love with Science: Championing Science for Everyone, Everywhere June 4 2019, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-falling-in-love-with-science-championing-science-for-everyone-everywhere/

Real Time Coverage @BIOConvention #BIO2019: June 4 Morning Sessions; Global Biotech Investment & Public-Private Partnerships, 6/4/2019, Philadelphia PA

Reporter: Stephen J Williams PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-june-4-morning-sessions-global-biotech-investment-public-private-partnerships/

Real Time Coverage @BIOConvention #BIO2019: Understanding the Voices of Patients: Unique Perspectives on Healthcare; June 4, 2019, 11:00 AM, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-understanding-the-voices-of-patients-unique-perspectives-on-healthcare-june-4/

Real Time Coverage @BIOConvention #BIO2019: Keynote: Siddhartha Mukherjee, Oncologist and Pulitzer Author; June 4 2019, 9AM, Philadelphia PA

Reporter: Stephen J. Williams, PhD. @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-keynote-siddhartha-mukherjee-oncologist-and-pulitzer-author-june-4-9am-philadelphia-pa/

Real Time Coverage @BIOConvention #BIO2019: Issues of Risk and Reproduceability in Translational and Academic Collaboration; 2:30-4:00 June 3, 2019, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-issues-of-risk-and-reproduceability-in-translational-and-academic-collaboration-230-400-june-3-philadelphia-pareal-time-coverage-bioconvention-bi/

Real Time Coverage @BIOConvention #BIO2019: What’s Next: The Landscape of Innovation in 2019 and Beyond. 3-4 PM June 3, 2019, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-whats-next-the-landscape-of-innovation-in-2019-and-beyond-3-4-pm-june-3-philadelphia-pa/

Real Time Coverage @BIOConvention #BIO2019: After Trump’s Drug Pricing Blueprint: What Happens Next? A View from Washington; June 3, 2019 1:00 PM, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-after-trumps-drug-pricing-blueprint-what-happens-next-a-view-from-washington-june-3-2019-100-pm-philadelphia-pa/

Real Time Coverage @BIOConvention #BIO2019: International Cancer Clusters Showcase June 3, 2019, Philadelphia PA

Reporter: Stephen J. Williams PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-international-cancer-clusters-showcase-june-3-philadelphia-pa/