Open data, compound repurposing, and rare diseases (ISCB)
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2017-02-16 ISCB departmental retreat
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Open data, compound repurposing, and rare diseases (ISCB)
- 1. Open data, compound repurposing, and rare diseases Andrew Su, Ph.D. @andrewsu asu@scripps.edu http://sulab.org February 16, 2017 Slides: slideshare.net/andrewsu
- 2. Raynaud disease and fish oil 2 Raynaud disease
- 3. Raynaud disease and fish oil 3 Raynaud disease Fish oil / EPA Abnormal platelet activity Abnormal blood viscosity High blood viscosity Elevated RBC rigidity Vasodilation Low blood triglycerides Increased prostacyclins
- 4. Raynaud disease and fish oil 4
- 5. “Undiscovered public knowledge” 5 Raynaud disease Fish oil / EPA Abnormal platelet activity Abnormal blood viscosity High blood viscosity Elevated RBC rigidity Vasodilation Low blood triglycerides Increased prostacyclins A C B B B B B B B
- 6. “Undiscovered public knowledge” 6 Raynaud disease Fish oil / EPA Abnormal platelet activity Abnormal blood viscosity High blood viscosity Elevated RBC rigidity Vasodilation Low blood triglycerides Increased prostacyclins A C B B B B B B B
- 8. Building a Network of BioThings (then) 8 Eicosapentaenoic acid Platelet aggregation Fatty Acid Edge = co-mention x 1000s article titles
- 9. Building a Network of BioThings (now) 9 Eicosapentaenoic acid Platelet aggregation Fatty Acid x 1000s article titles x 26 million articles… … and full abstracts decreases Edge = co-mention = PubChem:446284 = Timnodonic acid
- 10. Information extraction 10 1: Identify all biomedical concepts 2: Identify relationships between concepts
- 11. 11 Pathways Diseases Proteins Variants Genes Drugs Goal: Assemble a network of biomedical entities that is comprehensive, current, computable, and traceable.
- 12. 12
- 13. 13 Pathways Diseases Proteins Variants Genes Drugs Goal: Assemble a network of biomedical entities that is comprehensive, current, computable, and traceable.
- 15. Question: Can a group of non-scientists collectively perform concept recognition in biomedical texts? 15
- 16. 16 Experts versus crowd for concept identification 593 PubMed abstracts 6,900 mentions of “disease concepts” F = 0.87F = 0.78 $$$
- 17. 17 Experts versus crowd for concept identification 593 PubMed abstracts 6,900 mentions of “disease concepts” F = 0.87F = 0.87 $$$ • 9 days • 145 workers • Total: $630.96
- 19. 20 Paid crowdsourcing • F = 0.84 • 28 days • 212 workers • Total cost: $0 $$$ • F = 0.87 • 9 days • 145 workers • Total: $630.96 “Help science, please” Citizen Science
- 20. Does Citizen Science scale? 21 1,000,000 articles * 10 AE / article 15,828 volunteers needed 10,275 AE * 365 days 212 annotators* 28 days AE = Annotation events = Number of annotation events per year Number of annotation events per year per volunteer
- 21. Does Citizen Science scale? 22 15,828 volunteers needed 200,000 volunteers 460,000 volunteers 37,000 volunteers 1,000,000+ volunteers
- 23. Rare disease case study #2 24
- 24. 25
- 25. 26 … but no obvious treatments
- 26. Mapping the biomedical network around NGLY1 27 NGLY1
- 28. 29 A preliminary view of the NGLY1- focused biological network 1,200 contributors 3,200 documents 787,400 annotations
- 29. 30 A preliminary view of the NGLY1- focused biological network A C B B B B B B B AB B B B B B B A B B B B B B B
- 35. 36 Biomedical research relies on effective PietroBellini https://flic.kr/p/k5jmja KNOWELDGE MANAGEMENT
- 36. Ben GoodChunlei Wu Shirley Willis Sebastien Lelong Andra Waagmeester Max Nanis Cyrus Afrasiabi Julia Turner Ginger Tsueng M2C M2C Louis Gioia Toby Li Karthik G Kevin Xin Jake Bruggemann Mike Mayers DR DR Julee Adesara Ramya Gamini Greg Stupp Sebastian Burgstaller Tim Putman Nuria Queralt Rosinach DRDR DR DR M2C The Crowds Funding