Building a massive biomedical knowledge graph with citizen science
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The document discusses the creation of a biomedical knowledge graph utilizing citizen science to improve access to published knowledge in the field. By engaging non-professional volunteers for annotation tasks, the initiative seeks to achieve the quality of professional annotators while addressing the increasing volume of biomedical literature. Future steps include implementing a relation extraction workflow and focusing on disease-specific knowledge capture missions.
Building a massive biomedical knowledge graph with citizen science
- 1. Building a massive biomedical knowledge graph with citizen science Benjamin Good The Scripps Research Institute @bgood Not paying attention? be a citizen scientist at http://mark2cure.org
- 2. High level goal: improve access to published knowledge 22 articles added to PubMed per year 1 every 30 seconds, more than million a year knowledge graph
- 3. Chemicals & drugs Genes Organisms Area of study Biological Process Auto! Knowledge Graph ~10,000 articles Ngly1 gene ? New drug candidate?
- 4. Knowledge graph problems • Assigning meaning to relations • Incorrect relations • Missing relations • …
- 5. Facts of life in computer processing of human language • False Positives and False Negatives always • Human annotators remain the gold standard • There are not nearly enough professional human annotators to process every document published 5 Not paying attention? be a citizen scientist at http://mark2cure.org
- 6. Observations • There are about 2.92 billion Internet users • Lots of them can read English 6 http://www.statista.com/statistics/273018/number-of-internet-users-worldwide/
- 7. Hypothesis • We can generate the equivalent of massive numbers of professional annotators by aggregating the labor of large numbers of non- professional CITIZEN SCIENTISTS!!! 7
- 8. Building a Knowledge Graph 1. Find mentions of concepts in text 2. Identify relationships between concepts 8
- 9. Before we try for citizens.. • Can non-scientists collectively identify concepts in biomedical texts with high quality? • We used the Amazon Mechanical Turk crowdsourcing platform to answer the question 9 Not paying attention? be a citizen scientist at http://mark2cure.org
- 11. Answer was yes • By combining the responses of multiple non- professional members of ‘the crowd’, we achieved equivalent quality to professional annotators Good et al. “Microtask crowdsourcing for disease mention annotation in pubmed abstracts.” Pacific Symposium on Biocomputing 2015 http://psb.stanford.edu/psb-online/proceedings/psb15/good.pdf
- 12. Mark2Cure.org
- 13. Same task, different context
- 14. Experiment 1 in progress Evaluating quality and quantity of volunteer annotators Goal is to complete about 600 abstracts, with 15 volunteers per abstract Almost there!
- 15. mark2cure experiment 1 Tasks/10 New users Launch Tweet Blog post San Diego Union Tribune Article 11:00am Feb. 9 5423, tasks complete 230 signups, 130 have completed a task Not paying attention? be a citizen scientist at http://mark2cure.org
- 16. Next steps • Implement and test a relation extraction workflow • Start disease-focused knowledge capture missions • First disease: NGLY1 deficiency • http://ngly1.org
- 17. Thanks to the mark2cure team! Max Nanis Andrew Su @bgood bgood@scripps.edu Ginger Tsueng Chunlei Wu Thank you to the citizen scientists making this possible!
- 18. Why do I Mark2Cure? In memory of my daughter who had Cystic Fibrosis Studied biology in college and I really miss it! My 4 year old daughter Phoebe is living with and battling rare disease. I have Ehlers Danlos Syndrome. I hope to help people learn about this painful and debilitating disorder, so that others like me can receive more effective medical care. I am retired, have a doctorate in medical humanities, and have two children with Gaucher disease. I am just looking for some way to put my education to use. To give back I Mark2Cure in memory of my son Mike who had type 1 diabetes. Take part in something that helps humanity.
- 20. Increase precision with voting 20 1 or more votes (K=1) This molecule inhibits the growth of a broad panel of cancer cell lines, and is particularly efficacious in leukemia cells, including orthotopic leukemia preclinical models as well as in ex vivo acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) patient tumor samples. Thus, inhibition of CDK9 may represent an interesting approach as a cancer therapeutic target especially in hematologic malignancies. K=2 This molecule inhibits the growth of a broad panel of cancer cell lines, and is particularly efficacious in leukemia cells, including orthotopic leukemia preclinical models as well as in ex vivo acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) patient tumor samples. Thus, inhibition of CDK9 may represent an interesting approach as a cancer therapeutic target especially in hematologic malignancies. K=3 This molecule inhibits the growth of a broad panel of cancer cell lines, and is particularly efficacious in leukemia cells, including orthotopic leukemia preclinical models as well as in ex vivo acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) patient tumor samples. Thus, inhibition of CDK9 may represent an interesting approach as a cancer therapeutic target especially in hematologic malignancies. K=4 This molecule inhibits the growth of a broad panel of cancer cell lines, and is particularly efficacious in leukemia cells, including orthotopic leukemia preclinical models as well as in ex vivo acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL) patient tumor samples. Thus, inhibition of CDK9 may represent an interesting approach as a cancer therapeutic target especially in hematologic malignancies. Aggregation function
- 21. AMT results: 589 abstracts compared to gold standard 21 F = 0.87, k = 6