Big Data in Biomedicine – An NIH Perspective
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The document presents a perspective on the evolution and disruption of biomedical research due to big data, emphasizing the transition from a non-existent data landscape in the 1980s to today's integrated, patient-centered health care approaches. It highlights the importance of open collaborative science, the increasing value of data analytics, and the need for cultural shifts in academia and funding to adapt to this digital transformation. Additionally, it discusses the challenges and opportunities for computational tools in bioinformatics and proposes the development of a 'commons' for data sharing and accessibility.
Big Data in Biomedicine – An NIH Perspective
- 1. Big Data in Biomedicine – An NIH Perspective Philip E. Bourne Ph.D., FACMI Associate Director for Data Science National Institutes of Health philip.bourne@nih.gov IEEE BIBM Nov 10 2015, Washington DC http://www.slideshare.net/pebourne
- 2. Perspective Structural bioinformatics researcher Former custodian of the PDB Obsessive about open science e.g., PLOS NIH-wide responsibility for developments in data science – responding to the disruption
- 4. Big Data in Biomedicine… This speaks to something more fundamental that more data … It speaks to new methodologies, new skills, new emphasis, new cultures, new modes of discovery …
- 5. Consider this change from my own career experience ….
- 6. The 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 The People: No name Technicians Industry recognition data scientists Academics Searls (ed) The Roots in Bioinformatics Series PLOS Comp Biol
- 7. Premise: We are entering a period of disruption in biomedical research and we should all be thinking about what this means to bioinformatics & biomedicine http://i1.wp.com/chisconsult.com/wp- content/uploads/2013/05/disruption-is-a- process.jpg http://cdn2.hubspot.net/hubfs/418817/disruption1.jpg
- 8. We are at a Point of Deception … Evidence: – Google car – 3D printers – Waze – Robotics – Sensors From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
- 9. Disruption: Example - Photography 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
- 10. Disruption: Biomedical Research Digitization of Basic & Clinical Research & EHR’s Deception We Are Here Disruption Demonetization Dematerialization Democratization Open science Patient centered health care
- 11. Disruptive Features: Sustainability Source Michael Bell http://homepages.cs.ncl.ac.uk/m.j.bell1/blog/?p=830
- 12. Disruptive Features: Reproducibility Changing Value of Scholarship (?)
- 13. “And that’s why we’re here today. Because something called precision medicine … gives us one of the greatest opportunities for new medical breakthroughs that we have ever seen.” President Barack Obama January 30, 2015 Disruptive Features – New Science
- 14. An Example of That Promise: Comorbidity Network for 6.2M Danes Over 14.9 Years Jensen et al 2014 Nat Comm 5:4022
- 15. What Are Some General Implications of Such a Future? Open collaborative science becomes of increasing importance The value of data and associated analytics becomes of increasing value to scholarship Opportunities exist to improve the efficiency of the research enterprise and hence fund more research Cooperation between funders will be needed to sustain the emergent digital enterprise Current training content and modalities will not match supply to demand Balancing accessibility vs security becomes more important yet more complex
- 16. How Should We Respond? Funders: Encourage change and facilitate an orderly transition Academic Leaders: Respond and facilitate a cultural shift Developers: Develop working environments that are more adaptive and capable of answers questions in a more efficient and hopefully accurate way Users: Use the above environments Publishers: Move beyond papers
- 17. Take an Example That is Central to What We Do Molecular Graphics Is It Optimal for Today’s Science? http://upload.wikimedia.org/wikipedia/commons/2/2e/M olecular-Graphics-GRIP-75-Console.jpg
- 18. Good News/Bad News Good News: – It is harder to think of a more powerful way to comprehend complex data – It has excited generations to the promise of science – It has adapted to changing technologies Bad News: – It is not an adaptive/extensible environment – It is not a collaborative environment – It is not an integrative environment – It is the curse of the ribbon BMC Bioinformatics 2005, 6:21
- 19. 1. A link brings up figures from the paper 0. Full text of PLoS papers stored in a database 2. Clicking the paper figure retrieves data from the PDB which is analyzed 3. A composite view of journal and database content results Is a database really different than a biological journal? PloS Comp Biol 2005 1(3) e34 4. The composite view has links to pertinent blocks of literature text and back to the PDB 1. 2. 3. 4. The Knowledge and Data Cycle
- 20. Take Another Example: The Raw Material of Structural Bioinformatics Is this the optimal starting point anymore?
- 21. Do data resources including the PDB best serve the needs of the user at this point?
- 22. Good News/Bad News for the PDB in this Changing Landscape Bad News: – Interface complex and uni-data oriented – Data accessible; methods accessible (sort of); but not together – Significant redundancy in services offered Good News: – Annotation! – Demand is increasing – Integrated with other data types – Restful services
- 23. General Problem Statement: How to insure a high quality annotated data source that provides the optimal environment for accessibility, integration and analysis by a broad community of diverse users?
- 24. The Commons is a shared virtual space which is FAIR: – Find – Access (use effectively) – Interoperate – Reuse An environment to find and catalyze the use of shared digital research objects The Commons Concept
- 25. The Developer or User Defines the Environment from the Appropriate Building Blocks
- 26. Public Beacons Host Content AMPLab 1000 Genomes Project Broad Institute ExAC Curoverse PGP, GA4GH Example Data EBI 1000 Genomes Project, UK10K, GoNL, EVS, GEUVADIS, UMCG Cardio GenePanel Google 1000 Genomes Project, Phase III, Illumina Platinum Genomes ISB Known VARiants NCBI NHLBI Exome Sequence Project OICR 55 cancer datasets SolveBio 56 public datasets UCSC ClinVar, LOVD, UniProt University of Leicester Cafe CardioKit, Cafe Variome Central WTSI IBD, Native American, Egyptian, UK10K Over ?? public datasets beaconized across 21 institutions 10s thousands of individuals
- 28. The Commons Components Computing environment – cloud or HPC (High Performance Computing) – supports access, utilization, sharing and storage of digital objects. Methods for Interoperability – enables connectivity, shareability and interoperability between digital objects. Digital object compliance model – describes the properties of digital objects that enables them to be discoverable and shareable.
- 31. The ability to store and share and compute on digital research objects Especially useful for large data sets that are not easily computed locally Scalable and Elastic Pay per use - Cost effective An environment that fosters collaboration The Commons Computing Environment: Cloud
- 32. Commons - Pilots The Cloud Credits - business model BD2K Centers MODs (Model Organism Databases) HMP Data and tools available in the cloud NCI Cloud Pilots & Genomic Data Commons
- 33. The PDB in the Commons Components: – Annotated collection of data files – API’s to access these data files – Example methods using these APIs Potential outcomes – Nothing happens? – A new breed of developer starts to use PDB data in new ways ? – The casual user has a broader set of services that previously? – Quality declines/increases?
- 34. I not only use all the brains I have, but all I can borrow. – Woodrow Wilson
- 36. NIHNIH…… Turning Discovery Into HealthTurning Discovery Into Health philip.bourne@nih.gov https://datascience.nih.gov/ http://www.ncbi.nlm.nih.gov/research/staff/bourne/
Editor's Notes
- #14: Photos: FC tweet; RK screen grab
- #15: 16 million hospital inpatient events (24.5% of total), 35 million outpatient clinic events (53.6% of total) and 14 million emergency department events (21.9% of total
- #27: on this slide we have a list of Beacon providers and the content that they're serving. so to date we have over 120 public datasets that have been made available via Beacons at 12 different institutions. So this represents data from 10s of thousands of individuals and theses metrics, the numbers of datasets and individuals that they represent
- #29: Digital object = data or analytics software