Name
Abstract | ||
|---|---|---|
Most pharmacogenomics knowledge is contained in the text of published studies, and is thus not available for automated computation. Natural Language Processing (NLP) techniques for extracting relationships in specific domains often rely on hand-built rules and domain-specific ontologies to achieve good performance. In a new and evolving field such as pharmacogenomics (PGx), rules and ontologies may not be available. Recent progress in syntactic NLP parsing in the context of a large corpus of pharmacogenomics text provides new opportunities for automated relationship extraction. We describe an ontology of PGx relationships built starting from a lexicon of key pharmacogenomic entities and a syntactic parse of more than 87 million sentences from 17 million MEDLINE abstracts. We used the syntactic structure of PGx statements to systematically extract commonly occurring relationships and to map them to a common schema. Our extracted relationships have a 70-87.7% precision and involve not only key PGx entities such as genes, drugs, and phenotypes (e.g., VKORC1, warfarin, clotting disorder), but also critical entities that are frequently modified by these key entities (e.g., VKORC1 polymorphism, warfarin response, clotting disorder treatment). The result of our analysis is a network of 40,000 relationships between more than 200 entity types with clear semantics. This network is used to guide the curation of PGx knowledge and provide a computable resource for knowledge discovery. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.jbi.2010.08.005 | Journal of Biomedical Informatics |
Keywords | Field | DocType |
pgx knowledge,pharmacogenomics knowledge,pharmacogenomics text,semantic network,relationship extraction pharmacogenomics natural language processing ontology knowledge acquisition data integration biological network text mining information extraction,pgx relationship,pharmacogenomics,ontology,key pgx entity,knowledge acquisition,information extraction,pgx statement,knowledge discovery,syntactic nlp parsing,key entity,natural language processing,relationship extraction,data integration,biological network,text mining,key pharmacogenomic entity,data integrity,semantics,pharmacogenetics,polymorphism | Data mining,Ontology,Computer science,Natural language processing,Artificial intelligence,Relationship extraction,Ontology (information science),Information retrieval,Semantic network,Information extraction,Knowledge extraction,Knowledge acquisition,Semantics | Journal |
Volume | Issue | ISSN |
43 | 6 | 1532-0480 |
Citations | PageRank | References |
48 | 1.84 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Coulet | 1 | 165 | 16.04 |
| Nigam Shah | 2 | 1380 | 107.49 |
| Yael Garten | 3 | 183 | 8.73 |
| Mark A Musen | 4 | 7141 | 766.74 |
| Russ B. Altman | 5 | 2500 | 456.07 |