ArticleJournal of biomedical informatics2012
A knowledge-driven conditional approach to extract pharmacogenomics specific drug-gene relationships from free text.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- PGxCorpus, a manually annotated corpus for pharmacogenomics.Scientific data · 2020Article
- ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.Molecules (Basel, Switzerland) · 2018Article
- eGARD: Extracting associations between genomic anomalies and drug responses from text.PloS one · 2017Article
- DTMiner: identification of potential disease targets through biomedical literature mining.Bioinformatics (Oxford, England) · 2016Article
- Learning the Structure of Biomedical Relationships from Unstructured Text.PLoS computational biology · 2015Article
- Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.BMC bioinformatics · 2015Article
- Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature.Journal of biomedical informatics · 2014Article
- PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- A knowledge-driven approach to extract disease-related biomarkers from the literature.BioMed research international · 2014Review
- Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature.Bioinformatics (Oxford, England) · 2013Article
- A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine.Journal of biomedical informatics · 2013Article
- Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing.BMC bioinformatics · 2013Article
- Discovery of novel biomarkers and phenotypes by semantic technologies.BMC bioinformatics · 2013Article
- A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2013Article
- An iterative searching and ranking algorithm for prioritising pharmacogenomics genes.International journal of computational biology and drug design · 2013Article
- Toward creation of a cancer drug toxicity knowledge base: automatically extracting cancer drug-side effect relationships from the literature.Journal of the American Medical Informatics Association : JAMIAArticle
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2 authors.
Funding
Abstract
An important task in pharmacogenomics (PGx) studies is to identify genetic variants that may impact drug response. The success of many systematic and integrative computational approaches for PGx studies depends on the availability of accurate, comprehensive and machine understandable drug-gene relationship knowledge bases. Scientific literature is one of the most comprehensive knowledge sources for PGx-specific drug-gene relationships. However, the major barrier in accessing this information is that the knowledge is buried in a large amount of free text with limited machine understandability. Therefore there is a need to develop automatic approaches to extract structured PGx-specific drug-gene relationships from unstructured free text literature. In this study, we have developed a conditional relationship extraction approach to extract PGx-specific drug-gene pairs from 20 million MEDLINE abstracts using known drug-gene pairs as prior knowledge. We have demonstrated that the conditional drug-gene relationship extraction approach significantly improves the precision and F1 measure compared to the unconditioned approach (precision: 0.345 vs. 0.11; recall: 0.481 vs. 1.00; F1: 0.402 vs. 0.201). In this study, a method based on co-occurrence is used as the underlying relationship extraction method for its simplicity. It can be replaced by or combined with more advanced methods such as machine learning or natural language processing approaches to further improve the performance of the drug-gene relationship extraction from free text. Our method is not limited to extracting a drug-gene relationship; it can be generalized to extract other types of relationships when related background knowledge bases exist.
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