Biomedical Information Extraction for Disease Gene Prioritization

• URL: http://arxiv.org/abs/2011.05188v2
• Date: Thu, 12 Nov 2020 16:56:12 GMT
• Title: Biomedical Information Extraction for Disease Gene Prioritization
• Authors: Jupinder Parmar, William Koehler, Martin Bringmann, Katharina Sophia Volz, Berk Kapicioglu
• Abstract summary: We introduce a biomedical information extraction pipeline that extracts biological relationships from text. We apply it to tens of millions of PubMed abstracts to extract protein-protein interactions (PPIs) and augment these extractions to a biomedical knowledge graph. We show that, despite already containing PPIs from an established structured source, augmenting our own IE-based extractions to the graph allows us to predict novel disease-gene associations with a 20% relative increase in hit@30.
• Score: 0.34998703934432673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a biomedical information extraction (IE) pipeline that extracts biological relationships from text and demonstrate that its components, such as named entity recognition (NER) and relation extraction (RE), outperform state-of-the-art in BioNLP. We apply it to tens of millions of PubMed abstracts to extract protein-protein interactions (PPIs) and augment these extractions to a biomedical knowledge graph that already contains PPIs extracted from STRING, the leading structured PPI database. We show that, despite already containing PPIs from an established structured source, augmenting our own IE-based extractions to the graph allows us to predict novel disease-gene associations with a 20% relative increase in hit@30, an important step towards developing drug targets for uncured diseases.

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