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Bioinformatics. 2005 Jun 1;21(11):2759-65. Epub 2005 Apr 6.

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

Author information

1
Department of Biochemistry and Molecular Biology, Georgetown University Medical Center, Washington, DC 20057, USA. zh9@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 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.

PMID:
15814565
DOI:
10.1093/bioinformatics/bti390
[Indexed for MEDLINE]
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