VIVO Keynote
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Pace of technology innovation, changes in publication, separating data generation from publishing insights. Given at the 2018 VIVO conference at Duke University.
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VIVO Keynote
- 1. Scientific Publication, Data Sharing, Learning Health and Incentives Warren A. Kibbe, Ph.D. Professor, Biostats & Bioinformatics Chief Data Officer, Duke Cancer Institute warren.kibbe@duke.edu @wakibbe #VIVO18
- 2. Take homes • Pace of consumer computing • Data generation is no longer the bottleneck • 4th Industrial Revolution is here • Biomedical research and medicine is a data enterprise • Data sharing as an accelerant
- 5. Access to data has changed-Epic
- 6. From Apple Health App
- 8. Science has evolved • The role of societies and journals have slowly evolved during the past 150 years • Our ability to generate data has rapidly evolved in the past 40 years • Sharing knowledge, primary data, observations are throttled by our current publication process
- 9. How did we get here • A slight digression taking a narrow slice of history, technology, and science
- 10. Printing First Knowledge Revolution – printing press and access to mass produced print.
- 11. First Industrial Revolution Humans having access to cheap energy to do work (steam) William M. Connolley - Picture of the "Puffing Billy" steam engine taken in the Science Museum. on 2004/03/13. https://en.wikipedia.org/wiki/Steam_engine taken May 2018
- 12. First Industrial Revolution In addition to changing manufacturing and transportation, steam changed printing Meggs, Philip B. A History of Graphic Design. John Wiley & Sons, Inc. 1998. (p 132)
- 13. Scientific Publication 1.0 Journals, primarily the output of scientific societies, began in the 1860s. Peer review became the norm after WWII First issue of Nature, 1869
- 14. Second Industrial Revolution Mass production, better materials (steel) and manufacturing, distribution of energy using electricity & petroleum Robert Friedrich Stieler (1847–1908) - alte Postkarte, https://www.basf.com/de/company/about-us/history/1865-1901.html
- 15. Third Industrial Revolution The Digital Revolution 2008-03-19 21:41 Transisto from Wikipedia 10 August 2016 Thomas Nguyen - Wikipedia Mike1024 from wikipedia - University of Warwick 2006
- 16. Fourth Industrial Revolution Industry 4.0 • Communications • Connectivity • Ubiquituous • Pervasive • Internet of Things • Embedded Sensors • Process Automation • Cloud Computing Mass access to data generation, processing, visualization
- 17. Impact on Biomedical Research Challenges and Opportunities – Workforce! – Ethics! – Data management! – New instrumentation & tech – Computing, Analytics, Visualization, Usability – Data Science Biomedical research is a data driven enterprise
- 18. Biomedical Science 2.0 Workforce – Need more Quantitative Scientists – Biologically aware Data Scientists – Data aware researchers – Formal and informal training – Incentives for team science – Professional recognition Everyone in biomedical research and in medicine needs to be ‘Data Savvy’
- 19. Biomedical Science 2.0 Data Management – Data provenance – Validated devices – Credit for individual & team contributions – Persistence & immutability – DOIs – Reproducible workflows Cloud computing and data commons have many of the features we need
- 20. Biomedical Science 2.0 Some features of 2.0 • EHRs are now deployed • Smart devices nearly ubiquitous • Broadband a ‘human right’ • Patient experience opportunities • Ability to scale research Data management is crucial
- 21. Biomedical Science 2.0 • Instrumentation – Next Gen Sequencing – Mass Spec (proteomics, metabolomics) – Digital Imaging (Pathology, Radiology) – High Throughput Screening – Sensors Massive reduction in the cost of generating datasets
- 22. Biomedical Science 2.0 • Computing & Data Science • Modeling at many size and time scales • Causal inference • Width vs Depth • Complex vs simple relationships • Usability • Visualization and Human Cognition Deep learning, networks, mechanism, prediction, testing and validation
- 23. Where does all that leave us?
- 24. Data Science
- 25. Data Science Hype Data is the new oil
- 26. History
- 29. Machine Learning and Data Analytics are mainstream 0 500 1000 1500 2000 2500 3000 3500 4000 4500 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Publications in PubMed, 12/29/17 Machine Learning Deep Learning
- 31. Sebastian Thrun
- 32. 4/3/2017
- 33. Back to Medicine
- 34. Understanding Cancer • Precision medicine will lead to fundamental understanding of the complex interplay between genetics, epigenetics, nutrition, environment and clinical presentation and direct effective, evidence-based prevention and treatment. Ramifications across many aspects of health care
- 35. This redefinition has been driven by improved biological understanding
- 36. This change has been driven by improved technology - sequencing, imaging, nanotech, drug developing, computing and the availability of data about patient response to therapy
- 41. Scientific publication 2.0 • Journals are fully digital • Work flow automated • Open dissemination after embargo • Open Access still not the norm • Role of datasets and data publication still in flux • Data sharing of primary data still not the norm
- 42. Scientific publication 2.0 • Observations (data!) are accumulating at a rapid pace • Insights, information, analytics, and knowledge follow and conform to more classic versions of peer review and publication • IMO - We need to separate data sharing from knowledge sharing
- 43. Validation • Validation and Harmonization of primary and secondary data is crucial, but does not need to be done through the current publication process
- 44. Data Sharing Index • We need metrics for data, software, algorithm use, usability, conformance • FAIR!
- 46. NIH • NIH Strategic Plan for Data Science https://datascience.nih.gov/sites/default/files /NIH_Strategic_Plan_for_Data_Science_Final _508.pdf • Written by the NIH Scientific Data Council • About data management and analytics • Misses the change from data generation to data analytics
- 47. NIH Chief Data Strategist LinkedIn, June 6, 2018
Notas do Editor
- The printing press enabled social change through widely disseminating print – this increased the value of literacy and made censorship of information harder.
- For the first time people living in a society have access to much more than just the amount of work that they can do with their muscles or with domesticated animals. This opened up the possibility of creating machines and machinery at a very different scale. It also started to improve transportation
- Semiconductors, VLSI, minturization, hardware and software, transition from analog to digital devices
- But people can make effective decisions on the same number of factors…
- How can we use machine learning and other techniques to reduce cognitive overload?
- +ve –ve protein expression levels, ALK- Anaplastic lymphoma kinase, Squamous is a cell type (epidermoid),