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Bioinformatics Advance Access published online on April 6, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti390
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received February 1, 2005
Revised March 10, 2005
Accepted March 11, 2005

Article

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

Z. Z. Hu 1*, M. Narayanaswamy 2, K. E. Ravikumar 2, K. Vijay-Shanker 3, and C. H. Wu 1

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

* To whom correspondence should be addressed.
Z. Z. Hu, E-mail: zh9{at}georgetown.edu


  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 due 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 data sets used in this study are available at http://pir.georgetown.edu/iprolink/.


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