Text Mining of Protein Phosphorylation Information Using a Generalizable Rule-Based Approach

Authors:
Manabu Torii;Cecilia N. Arighi;Qinghua Wang;Cathy H. Wu;K. Vijay-Shanker
Affiliations:
Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA;Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA;Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA;Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA;Department of Computer and Information Sciences and Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

Literature-based annotation of protein phosphorylation is the focus of many biological databases, as phosphorylation is a global regulator of cellular activity. To speed up manual curation of phosphorylation information, text mining technology has been utilized. In this paper, we report our ongoing effort to enhance RLIMS-P, a rule-based information extraction (IE) system to identify protein phosphorylation information in scientific literature. Despite the high accuracy attained by RLIMS-P, the use of elaborated patterns and rules resulted in a substantial effort for system development and maintenance. To mitigate this challenge, we redesigned RLIMS-P and integrated new natural language processing (NLP) techniques. It has also been adapted to mine full-text articles and generalized to be able to exploit common features for different post-translational modifications (PTMs). The updated RLIMS-P (version 2.0) was evaluated on abstracts in the publicly available BioNLP GENIA event extraction (GE) corpus, and achieved F-scores of 0.92 and 0.96 for phosphorylation substrate and site, respectively. On a full-text corpus developed in-house, it achieved F-scores of 0.91 and 0.92 for substrate and site, and 0.88 for kinase. The system was applied to the PubMed Central (PMC) Open Access Subset, and promising results have been obtained in mining the full-text articles. RLIMS-P focuses on protein phosphorylation information, but its new design would be generalizable for other PTM types. RLIMS-P version 2.0 is available at: http://proteininformationresource.org/rlimsp/.