Literature mining and database annotation of protein phosphorylation using a rule-based system

  • Authors:

  • Z. Z. Hu;M. Narayanaswamy;K. E. Ravikumar;K. Vijay-Shanker;C. H. Wu

  • Affiliations:

  • Department of Biochemistry and Molecular Biology, Georgetown University Medical Center Washington, DC 20057, USA;AU-KBC Research Centre, Anna University Chennai 600044, India;AU-KBC Research Centre, Anna University Chennai 600044, India;Department of Computer and Information Sciences, University of Delaware Newark, DE 19716, USA;Department of Biochemistry and Molecular Biology, Georgetown University Medical Center Washington, DC 20057, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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Abstract

Motivation: A large volume of experimental data on protein phosphorylation is buried in the fast-growing PubMed literature. While of great value, such information is limited in databases owing to the laborious process of literature-based curation. Computational literature mining holds promise to facilitate database curation. Results: A rule-based system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was used to extract protein phosphorylation information from MEDLINE abstracts. An annotation-tagged literature corpus developed at PIR was used to evaluate the system for finding phosphorylation papers and extracting phosphorylation objects (kinases, substrates and sites) from abstracts. RLIMS-P achieved a precision and recall of 91.4 and 96.4% for paper retrieval, and of 97.9 and 88.0% for extraction of substrates and sites. Coupling the high recall for paper retrieval and high precision for information extraction, RLIMS-P facilitates literature mining and database annotation of protein phosphorylation. Availability: The program is available on request from the authors. The phosphorylation patterns and datasets used in this study are available at http://pir.georgetown.edu/iprolink/ Contact: zh9@georgetown.edu