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

Emily K. Mallory | Zendy; Ce Zhang | Zendy; Christopher Ré | Zendy; Russ B. Altman | Zendy

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

Author(s) -

Emily K. Mallory,

Ce Zhang,

Christopher Ré,

Russ B. Altman

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv476

Subject(s) - computer science , sentence , gene , extractor , relation (database) , computational biology , gene interaction , relationship extraction , bioinformatics , information retrieval , data mining , natural language processing , genetics , biology , process engineering , engineering

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.

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