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This article is part of the supplement: Machine Learning for Biomedical Literature Analysis and Text Retrieval in the International Conference on Machine Learning and Applications 2011

Open Access Research

Literature mining of protein-residue associations with graph rules learned through distant supervision

KE Ravikumar1*, Haibin Liu1, Judith D Cohn2, Michael E Wall2 and Karin Verspoor13

Author Affiliations

1 University of Colorado School of Medicine, Aurora, CO 80045, USA

2 Los Alamos National Laboratory, Los Alamos, NM 87545, USA

3 National ICT Australia Victoria Research Lab, Melbourne, Australia

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Journal of Biomedical Semantics 2012, 3(Suppl 3):S2  doi:10.1186/2041-1480-3-S3-S2

Published: 5 October 2012

Abstract

Background

We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model.

Results

The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods.

Conclusions

The primary contributions of this work are to (1) demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2) show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.