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DTSTART;TZID=America/Denver:20231112T164500
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UID:submissions.supercomputing.org_SC23_sess430_ws_cafcw101@linklings.com
SUMMARY:Constructing a Large-Scale Biomedical Knowledge Graph and Its Appl
 ications in Drug Discovery
DESCRIPTION:Jinfeng Zhang (Florida State University, Insilicom LLC)\n\nIn 
 the past few decades, the biomedical research community has acquired a wea
 lth of knowledge, much of which is stored in scientific literature as unst
 ructured text. Converting this text into structured form is crucial for de
 veloping new methodologies and applications that can fully utilize this kn
 owledge. To achieve this goal, two basic problems must be addressed: named
  entity recognition (NER) and relation extraction (RE). NER involves ident
 ifying the concepts or entities in texts, such as diseases, genes/proteins
 , and chemical compounds. RE, on the other hand, aims to extract the relat
 ionships between these entities. The information extracted from NER and RE
  can be used to create knowledge graphs, where nodes represent entities in
  the text and edges represent their relationships. This presentation will 
 discuss our team's work on the LitCoin NLP Challenge organized by NIH, for
  which we were awarded first place. Using pipelines developed for the chal
 lenge, we processed all PubMed articles and created a large-scale biomedic
 al knowledge graph. The accuracy of this large-scale relation extraction i
 s estimated using manual verification of a sample of the extracted data an
 d found to be at the human annotation level. We also incorporated relation
  information from 40 public databases and relations inferred from publicly
  available genomics datasets. Our knowledge graph consists of over 11 mill
 ion entities and more than 40 million relations. We have developed versati
 le query functions and knowledge discovery tools for accessing and mining 
 structured data in the knowledge graph. Finally, we will discuss some drug
  discovery-related applications enabled by this large-scale knowledge grap
 h.\n\nTag: Applications, State of the Practice\n\nRegistration Category: W
 orkshop Reg Pass\n\nSession Chairs: Lynn Borkon (Frederick National Labora
 tory for Cancer Research); Sally Ellingson (University of Kentucky); Sean 
 Hanlon (National Institutes of Health (NIH), National Cancer Institute (NC
 I)); Patricia Kovatch (Icahn School of Medicine at Mount Sinai); and Eric 
 Stahlberg (MD Anderson Cancer Center, University of Texas)\n\n
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