Large-scale extraction of gene interactions from full-text literature using DeepDive

Bioinformatics
Emily K MalloryRuss B Altman

Abstract

A complete repository of gene-gene interactions is key for understanding cellular processes, human disease and drug response. These gene-gene interactions include both protein-protein interactions and transcription factor interactions. The majority of known interactions are found in the biomedical literature. Interaction databases, such as BioGRID and ChEA, annotate these gene-gene interactions; however, curation becomes difficult as the literature grows exponentially. DeepDive is a trained system for extracting information from a variety of sources, including text. In this work, we used DeepDive to extract both protein-protein and transcription factor interactions from over 100,000 full-text PLOS articles. We built an extractor for gene-gene interactions that identified candidate gene-gene relations within an input sentence. For each candidate relation, DeepDive computed a probability that the relation was a correct interaction. We evaluated this system against the Database of Interacting Proteins and against randomly curated extractions. Our system achieved 76% precision and 49% recall in extracting direct and indirect interactions involving gene symbols co-occurring in a sentence. For randomly curated extractions, the system...Continue Reading

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Citations

Sep 29, 2020·Frontiers in Cell and Developmental Biology·Nadeesha PereraFrank Emmert-Streib
Sep 30, 2018·Proceedings of the National Academy of Sciences of the United States of America·Byung-Kwon ChoiOlivier Lichtarge
Jul 9, 2020·Computational and Structural Biotechnology Journal·David N Nicholson, Casey S Greene
Dec 21, 2019·Journal of Biomedical Informatics·Saman FarahmandKourosh Zarringhalam
Aug 16, 2019·Emerging Topics in Life Sciences·J Harry Caufield, Peipei Ping
Jul 3, 2021·Proteomes·Jagajjit Sahu

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