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Eight {So Far} Things I Wish I had Thought About 40 Years Ago


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ISMB 2018 Student Council Keynote, Chicago, July 6, 2018

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Eight {So Far} Things I Wish I had Thought About 40 Years Ago

  1. 1. Eight {So Far} Things I Wish I Had Thought About 40 Years Ago Philip E. Bourne PhD, FACMI Stephenson Chair of Data Science Director, Data Science Institute Professor of Biomedical Engineering 1 @pebourne ISMB Student Council July 6, 2018
  2. 2. This is not a lecture it is a discussion …. Acknowledgement: The context for this discussion draws from the nearly 100 x Ten Simple Rules.. Which takes us to Take Home One & Two 2
  3. 3. Take home one … Science is a team sport … Your long term success will depend as much on the ability to build and maintain a team as it will on your individual accomplishments 3
  4. 4. Take home two … • Its more … • Collaboration • Management skills • Communication • Verbal • Written • Administrative ability • All impact productivity • Grants • Papers • Teaching awards • Editorial and committee work • Etc. 4 Ten Simple Rules for Getting Ahead as a Computational Biologist in Academia. PLoS Comput Biol 7(1): e1002001.
  5. 5. Take home three … (from Hamming) Work on the most important problems in your field {as you believe they will be in years to come} 5 Erren TC, Cullen P, Erren M, Bourne PE (2007) Ten Simple Rules for Doing Your Best Research, According to Hamming. PLOS Comput Biol 3(10): e213
  6. 6. What is to come can be extrapolated from past history 6
  7. 7. The Past History of Computational Biomedicine According to Bourne 1980s 1990s 2000s 2010s 2020 Discipline: Unknown Expt. Driven Emergent Over-sold A Service A Partner A Driver The Raw Material: Non-existent Limited /Poor More/Ontologies Big Data/Siloed Open/Integrated Unstructured The People: No name Technicians Industry recognition Academics Data Scientists Searls (ed) The Roots in Bioinformatics Series PLOS Comp Biol 7 As described to the Advisory Committee to the NIH Director
  8. 8. You are entering the field at a time when society at large is catching up … The good news ... Many opportunities The bad news ... Many opportunities outside of biomedicice 8
  9. 9. 9 content/uploads/2009/10/Fourth_Paradigm.pdf 856825353645559808
  10. 10. 10
  11. 11. What of the future? One view is the 6D’s 11
  12. 12. Digitization Deception Disruption Demonetization Dematerialization Democratization Time Volume,Velocity,Variety Digital camera invented by Kodak but shelved Megapixels & quality improve slowly; Kodak slow to react Film market collapses; Kodak goes bankrupt Phones replace cameras Instagram, Flickr become the value proposition Digital media becomes bona fide form of communication From a presentation to the Advisory Board to the NIH Director Example - photography 12
  13. 13. Maybe follow the emergent data & analytics… • A few examples: • Imaging – biggest success in machine learning • EHR’s – still the wild west, but becoming civilized • Integration with environmental data • Cancer • Autism • Prevention – social media • Mental health • Global health • Pandemics • Biocomplexity 13
  14. 14. Think about the technologies and how they will change … Will biomedical research become more like Airbnb? 14 Should biomedical research be like Airbnb? PLOS Biol 15(4): e2001818
  15. 15. Open platforms will digitally integrate the scholarly workflow for human and machine analysis Should biomedical research be Like Airbnb? doi: 10.1371/journal.pbio.2001818 15
  16. 16. Take home four … The most compelling science still needs money 16
  17. 17. Look to what is being funded (apologies for the US centricity) • Moonshot – cancer genomics • MODs old dog; new tricks • Human Microbiome Project – a gut feel • TOPMed - genotype to phenotype • All-of-Us – precision medicine • ECHO – child health and the environment • BRAIN - neuroscience 17
  18. 18. Take home five … Treat others as you treat yourself Trust me, If you don’t it will come back to haunt you 18
  19. 19. Take home six … Follow your heart not your brain 19
  20. 20. Take home seven … Diversity of research is a relative term … Figure out where you are comfortable on the spectrum … More diversity means less depth however smart you are 20
  21. 21. Take home eight … its about balance 21 ISMB 2006 ISMB 2009
  22. 22. Discussion •Does this resonate with you? •What is missing from your perspective? •What could ISCB do to help that it is not? 22
  23. 23. References • Russ Altman – Translational Bioinformatics Year in Review • Bourne PE (2011) Ten Simple Rules for Getting Ahead as a Computational Biologist in Academia. PLOS Comput Biol 7(1): e1002001 23