Mar 4, 2018 - Bioinformatics in 2018: Data Science, Artificial. Intelligence ... Public. 2. * ML=Machine Learning, DL=Deep Learning, AI=Artificial Intelligence ...
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Bioinformatics in 2018: Data Science, Artificial Intelligence and the Digital Revolution Philippe MARC, Global Head Integrated Data Integration B2B Bioinfoinformatics Strategy Meeting by Proventa March 5th, 2018
Bioinformatics in 2018: Data Science, Artificial Intelligence and the Digital Revolution
Bioinformatics according to Wikipedia: the old Art of Omics
Created on March 4th 2018 based on https://en.wikipedia.org/wiki/Bioinformatics (stop words excluded)
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Typical questions in Bio-informatics 1. Data access, analysis, and visualization – What experiments/studies were conducted – What was the result of the experiment/study
2. Efficacy of drugs – – – –
Q? Is my compound optimal (Chemo-informatics) Q? Study endpoints - classical and “digital” Q? Mechanism of action Q? Human relevance of in vitro and animal data
3. Safety of drugs – – – –
Q? Study read-out and outcome Q? Off-target effects Q? Mechanism of toxicity Q? Human relevance of in vitro and animal data
4. Historical values and searching the past – Q? Normal ranges – Where have we seen this before?
5. Cross-dataset analysis – Is this related to that? – Q? using meta-analysis, ML*, DL*, AI*
6. Biomarkers – Q? Genetics and stratification – Q? how to use RNA, micro RNAs, proteins, metabolomics, imaging data
7. Project portfolio and operations – At what stage is this project? Milestones? People? Competitors? Critical issues? – Q? Timelines/resources
8. And many others – How to visualize/communicate my results? – Where to compute using what algorithm? – …
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* ML=Machine Learning, DL=Deep Learning, AI=Artificial Intelligence
Key enablers are the tracks on today’s agenda
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Bioinformatics in 2018: Data Science, Artificial Intelligence and the Digital Revolution
The promise is still the same…
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…but the scale is changing… Recent evolution of genetics DBs…
… and a glimpse at what is coming
https://macarthurlab.org/2017/02/27/the-genome-aggregation-database-gnomad/
Keith Nangle and Mike Furness, Feb 2018, http://www.pistoiaalliance.org/nangle-furness-ddw/
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…and the way to crack it is evolving
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Credit: Monica Rogati, Aug 2017, https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007
Bioinformatics in 2018: Data Science, Artificial Intelligence and the Digital Revolution
Emerging technologies (July 2017)
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https://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017/
ML* is “in production” in Pharmas
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Other industries are using same methods…
https://www.kaggle.com/surveys/2017
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... and they face similar challenges
https://www.kaggle.com/surveys/2017
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Where can we apply ML/DL/AI* in the R&D part of the Pharma business?
Aspirations Innovate efficient and safe drugs for patients
Data we can easily use today Screening on large chemistry space
Maximize speed and success rate during R&D Change practice of medicine
Image and movie collections Multi-pan-Omics on large patient cohorts Continuous data stream from digital devices RWEs from eHR, insurance claims
Questions J
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* ML=Machine Learning, DL=Deep Learning, AI=Artificial Intelligence
Bioinformatics in 2018: Data Science, Artificial Intelligence and the Digital Revolution
“Digital Revolution”: the cynical view The Digital Revolution “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” Dan Ariely, Jan 2013
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“Digital Revolution”: the optimistic view
Alison E. Berman, Jason Dorrier and David J. Hill, https://singularityhub.com/2016/04/05/how-to-think-exponentially-and-better-predict-the-future/
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We can be optimistic! Drivers
Challenges
ising
Digital mindset ra
FAIRification of data
Pharma
Open source entations of key em pl im rithms methods and algo wer Computational po tion ita lim a t no (usually) anymore
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ending The right team: bld Data an al ic og ol in Bi Science skills : Data governance y ac iv pr s, responsibilitie ts and inform consen n The right questio gh ou en to ed at associ ta information-rich da
But don’t forget! What marketing would sell: computers will take care of everything!
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What it is really: help for decision making by experts
Presentation available on Research Gate https://www.researchgate.net/profile/Philippe_Marc2
Thank you
