Inferring Drug-Protein⁻Side Effect Relationships from Biomedical Text

Genes
Min SongJeong-Hoon Lee

Abstract

Although there are many studies of drugs and their side effects, the underlying mechanisms of these side effects are not well understood. It is also difficult to understand the specific pathways between drugs and side effects. The present study seeks to construct putative paths between drugs and their side effects by applying text-mining techniques to free text of biomedical studies, and to develop ranking metrics that could identify the most-likely paths. We extracted three types of relationships-drug-protein, protein-protein, and protein⁻side effect-from biomedical texts by using text mining and predefined relation-extraction rules. Based on the extracted relationships, we constructed whole drug-protein⁻side effect paths. For each path, we calculated its ranking score by a new ranking function that combines corpus- and ontology-based semantic similarity as well as co-occurrence frequency. We extracted 13 plausible biomedical paths connecting drugs and their side effects from cancer-related abstracts in the PubMed database. The top 20 paths were examined, and the proposed ranking function outperformed the other methods tested, including co-occurrence, COALS, and UMLS by P@5-P@20. In addition, we confirmed that the paths are no...Continue Reading

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Citations

Apr 22, 2020·Drug Development Research·Kanica Sachdev, Manoj K Gupta

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Software Mentioned

MetaMap
UMLS
SIDER
ABNER
Banner
COALS
LingPipe
CoreNLP
Stanford CoreNLP

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