title: An iterative searching and ranking algorithm for prioritising pharmacogenomics genes.
abstract: Pharmacogenomics (PGx) studies are to identify genetic variants that may affect drug efficacy and toxicity. A machine understandable drug-gene relationship knowledge is important for many computational PGx studies and for personalised medicine. A comprehensive and accurate PGx-specific gene lexicon is important for automatic drug-gene relationship extraction from the scientific literature, rich knowledge source for PGx studies. In this study, we present a bootstrapping learning technique to rank 33,310 human genes with respect to their relevance to drug response. The algorithm uses only one seed PGx gene to iteratively extract and rank co-occurred genes using 20 million MEDLINE abstracts. Our ranking method is able to accurately rank PGx-specific genes highly among all human genes. Compared to randomly ranked genes (precision: 0.032, recall: 0.013, F1: 0.018), the algorithm has achieved significantly better performance (precision: 0.861, recall: 0.548, F1: 0.662) in ranking the top 2.5% of genes.
authors: Medical Informatics Division, Case Western Reserve University, Cleveland, OH 44106, USA. rxx@case.edu
authors: Rong
authors: R
authors: Xu
authors: Quanqiu
authors: Q
authors: Wang
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D015195:Drug Design; D023281:Genomics / Q000379:methods*; D006801:Humans; D009323:Natural Language Processing; D010597:Pharmacogenetics / Q000379:methods*; D000069437:Pharmacological Phenomena; D057285:Precision Medicine