title: Improving the prediction of pharmacogenes using text-derived drug-gene relationships.
abstract: A critical goal of pharmacogenomics research is to identify genes that can explain variation in drug response. We have previously reported a method that creates a genome-scale ranking of genes likely to interact with a drug. The algorithm uses information about drug structure and indications of use to rank the genes. Although the algorithm has good performance, its performance depends on a curated set of drug-gene relationships that is expensive to create and difficult to maintain. In this work, we assess the utility of text mining in extracting a network of drug-gene relationships automatically. This provides a valuable aggregate source of knowledge, subsequently used as input into the algorithm that ranks potential pharmacogenes. Using a drug-gene network created from sentence-level co-occurrence in the full text of scientific articles, we compared the performance to that of a network created by manual curation of those articles. Under a wide range of conditions, we show that a knowledge base derived from text-mining the literature performs as well as, and sometimes better than, a high-quality, manually curated knowledge base. We conclude that we can use relationships mined automatically from the literature as a knowledgebase for pharmacogenomics relationships. Additionally, when relationships are missed by text mining, our system can accurately extrapolate new relationships with 77.4% precision.
authors.affiliation: Stanford Biomedical Informatics Training Program, Stanford University, Stanford, CA 94305, USA.
authors.forename: Yael
authors.initials: Y
authors.lastname: Garten
authors.forename: Nicholas P
authors.initials: NP
authors.lastname: Tatonetti
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D057225:Data Mining / Q000706:statistics & numerical data; D006801:Humans; D051188:Knowledge Bases; D010597:Pharmacogenetics / Q000706:statistics & numerical data*