ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2010
Improving the prediction of pharmacogenes using text-derived drug-gene relationships.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge.Current protocols · 2021Article
- Combining entity co-occurrence with specialized word embeddings to measure entity relation in Alzheimer's disease.BMC medical informatics and decision making · 2019Article
- The research on gene-disease association based on text-mining of PubMed.BMC bioinformatics · 2018Article
- Learning from biomedical linked data to suggest valid pharmacogenes.Journal of biomedical semantics · 2017Article
- Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.Scientific reports · 2015Article
- Progress towards the integration of pharmacogenomics in practice.Human genetics · 2015Review
- Combining heterogenous data for prediction of disease related and pharmacogenes.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2014Article
- Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.Pharmacology & therapeutics · 2013Review
- Using PharmGKB to train text mining approaches for identifying potential gene targets for pharmacogenomic studies.Journal of biomedical informatics · 2012Article
- A knowledge-driven conditional approach to extract pharmacogenomics specific drug-gene relationships from free text.Journal of biomedical informatics · 2012Article
- Mining the pharmacogenomics literature--a survey of the state of the art.Briefings in bioinformatics · 2012Article
- Ranking gene-drug relationships in biomedical literature using Latent Dirichlet Allocation.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2012Article
- Databases in the area of pharmacogenetics.Human mutation · 2011Review
- BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs.BMC bioinformatics · 2011Article
- Recent progress in automatically extracting information from the pharmacogenomic literature.Pharmacogenomics · 2010Review
- Discovering drug-drug interactions: a text-mining and reasoning approach based on properties of drug metabolism.Bioinformatics (Oxford, England) · 2010Article
- Teaching computers to read the pharmacogenomics literature ... so you don't have to.Pharmacogenomics · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.