Building and mining a heterogeneous biomedical knowledge graph
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Building and mining a heterogeneous biomedical knowledge graph
- 1. Building and mining a heterogenous biomedical knowledge graph June 23, 2020 Slides: slideshare.net/andrewsu Andrew Su, Ph.D. @andrewsu http://sulab.org Q19857262
- 3. CML Pathway KEGG:hsa05220 Bcr-Abl UniProtKB:A9UF02 c-Kit UniProtKB:P10721 Imatinib CHEBI:45783 Mast Cell Degranulation GO:0043303 Chronic myelogenous leukemia DOID:8552 Asthma DOID:2841 inhibits inhibits part of part of causescauses treats treats? 3 3
- 4. 4
- 5. 5 Citation: https://doi.org/10.7554/eLife.26726 Method based on https://doi.org/10.1109/ASONAM.2011.112
- 6. “ 6 Daniel Himmelstein, https://think-lab.github.io/d/102/ We obtained and integrated data from 29 publicly available resources to create Hetionet v1.0. The hetnet contains 47,031 nodes of 11 types and 2,250,197 relationships of 24 types.
- 7. Algorithm: KB: Rephetio MINERVA, CBR, etc. Text-mining Semantic MEDLINE DB Crowd-curated Distributed KG 7
- 8. Semantic MEDLINE (SemMedDB) • Processed all PubMed abstracts (30 million+) every 6-12 months • 90 million+ triples covering key biomedical entity types (genes, drugs, diseases, anatomy, physiological processes, etc.) • All triples anchored to a publication (and publication date) 8 https://skr3.nlm.nih.gov/SemMedDB/
- 9. Time-resolved prediction analysis 9 1950 1970 1990 2010 1960 1980 2000 Drug-disease 1 Drug-disease 2 Drug-disease 3 Drug-disease 4 Drug-disease 5 Drug-disease 6 … Drug-disease n Approval dates KB year Train Test
- 10. Time-resolved prediction analysis 10 1950 1970 1990 2010 1960 1980 2000 Drug-disease 1 Drug-disease 2 Drug-disease 3 Drug-disease 4 Drug-disease 5 Drug-disease 6 … Drug-disease n Approval dates KB year Train Test
- 11. Time-resolved prediction analysis 11 1950 1970 1990 2010 1960 1980 2000 Drug-disease 1 Drug-disease 2 Drug-disease 3 Drug-disease 4 Drug-disease 5 Drug-disease 6 … Drug-disease n Approval dates KB year
- 12. Time-resolved prediction analysis 12 1950 1970 1990 2010 1960 1980 2000 Drug-disease 1 Drug-disease 2 Drug-disease 3 Drug-disease 4 Drug-disease 5 Drug-disease 6 … Drug-disease n Approval dates KB year
- 13. Time-resolved prediction analysis 13 1950 1970 1990 2010 1960 1980 2000 Drug-disease 1 Drug-disease 2 Drug-disease 3 Drug-disease 4 Drug-disease 5 Drug-disease 6 … Drug-disease n Approval dates KB year
- 14. Time-resolved prediction analysis 14 Area Under ROC https://doi.org/10.1186/s12859-019-3297-0
- 15. Time-resolved prediction analysis 15 Area Under ROC Area Under PRC https://doi.org/10.1186/s12859-019-3297-0 Key findings • Computational drug repurposing using KB reasoning is hard • We lose almost all predictive signal at < 5 years in the future
- 16. Time-resolved prediction analysis 16 Area Under ROC https://doi.org/10.1186/s12859-019-3297-0 Key findings • Computational drug repurposing using KB reasoning is hard • We lose almost all predictive signal at < 5 years in the future • Identified four edge types that are sensitive to information gain/loss compound – TREATS – disease compound – RELATEDTO – compound anatomy – LOCATIONOF – diseasedisease – ASSOCIATEDWITH – disease
- 17. Algorithm: KB: Rephetio MINERVA, CBR, etc. Crowd-curatedText-mining Semantic MEDLINE DB Distributed KG 17
- 18. is to data is to text biomedical Provide a database of the world’s knowledge that anyone can edit - Denny Vrandečić 18
- 20. Wikidata as a drug repositioning KB 20
- 21. Wikidata as a drug repositioning KB 21
- 22. Wikidata as a drug repositioning KB 22
- 23. Wikidata as a drug repositioning KB Key findings • Wikidata is comparable in size, scope, structure to be suitable for computational drug repositioning • Results improve over time as the KB content increases in size and quality 23 2017-01-16 2018-02-05 2019-09-13 https://doi.org/10.7554/eLife.52614
- 24. Algorithm: KB: Rephetio MINERVA, CBR, etc. Crowd-curated Distributed KGText-mining Semantic MEDLINE DB 24
- 25. Data federation is common, and sometimes necessary 25 20,000 genes 20,000genes Gene correlation matrices Health data Licensing restrictions The (Re)usable Data Project http://reusabledata.org/ Columbia Open Health Data http://cohd.io/
- 28. Notebooks ● Github ● colab Autonomous query planning and API call execution 28
- 29. Querying a distributed KG 29 Query template Imatinib [Disease] [Gene] [Pathway] Key question • Is this architecture amenable to KB completion?
- 30. Algorithm: KB: Evidence: Rephetio MINERVA, CBR, etc. Text-mining Crowd-curated Distributed KG Semantic MEDLINE DB DrugMechDB 30
- 31. CML Pathway KEGG:hsa05220 Bcr-Abl UniProtKB:A9UF02 c-Kit UniProtKB:P10721 Imatinib CHEBI:45783 Mast Cell Degranulation GO:0043303 Chronic myelogenous leukemia DOID:8552 Asthma DOID:2841 inhibits inhibits part of part of causescauses treats treats? 31
- 32. fomepizole Compound Alcohol dehydrogenase 1A Gene Ethanol oxidation Pathway formic acid Compound Methanol poisoning Disease MESH:D000077604 UniProt:P07327 REACT:R-HSA-71384 MESH:C030544 MESH:D000138 Inhibits Part of Produces Causes Indication: Fomepizole – TREATS – Methanol poisoning DrugMechDB – a database of drug mechanisms https://github.com/SuLab/DrugMechDB 32
- 33. Acknowledgements Mike Mayers Núria Queralt-Rosinach Toby Li Mike Mayers Andra Waagmeester (Micelio) Sabah Ul-Hasan Ginger Tsueng Roger Tu Greg Stupp Ben Good Tim Putman Sebastian Burgstaller-Muehlbacher Lynn Schriml (Univ. Maryland) Kevin Hybiske (Univ. Washington) Kevin Xin Chunlei Wu Marco Alvarado Jerry Zhou Semantic MEDLINE DB DrugMechDB: Mike Mayers 33 Funding