ArticleInternational journal of computational biology and drug design2013
An iterative searching and ranking algorithm for prioritising pharmacogenomics genes.
Article in International journal of computational biology and drug design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed, 3 citations in OpenAlex.
- A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine.Journal of biomedical informatics · 2013Article
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2 authors at 1 institution in 1 country.
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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.
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