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Bioinformatics. 2019 Jun 14. pii: btz490. doi: 10.1093/bioinformatics/btz490. [Epub ahead of print]

CoCoScore: Context-aware co-occurrence scoring for text mining applications using distant supervision.

Author information

1
Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen N, Denmark.

Abstract

MOTIVATION:

Information extraction by mining the scientific literature is key to uncovering relations between biomedical entities. Most existing approaches based on natural language processing extract relations from single sentence-level co-mentions, ignoring co-occurrence statistics over the whole corpus. Existing approaches counting entity co-occurrences ignore the textual context of each co-occurrence.

RESULTS:

We propose a novel corpus-wide co-occurrence scoring approach to relation extraction that takes the textual context of each co-mention into account. Our method, called CoCoScore, scores the certainty of stating an association for each sentence that co-mentions two entities. CoCoScore is trained using distant supervision based on a gold-standard set of associations between entities of interest. Instead of requiring a manually annotated training corpus, co-mentions are labeled as positives/negatives according to their presence/absence in the gold standard. We show that CoCoScore outperforms previous approaches in identifying human disease-gene and tissue-gene associations as well as in identifying physical and functional protein-protein associations in different species. CoCoScore is a versatile text mining tool to uncover pairwise associations via co-occurrence mining, within and beyond biomedical applications.

AVAILABILITY:

CoCoScore is available at: https://github.com/JungeAlexander/cocoscore.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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