Literature mining and database annotation of protein phosphorylation using a rule-based system.
BACKGROUND
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.
Hu
Z Z
ZZ
Department of Biochemistry and Molecular Biology, Georgetown University Medical Center, Washington, DC 20057, USA. zh9@georgetown.edu
Narayanaswamy
M
M
Ravikumar
K E
KE
Vijay-Shanker
K
K
Wu
C H
CH
D000043:Abstracting and Indexing / Q000379:methods; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010506:Periodicals as Topic; D010766:Phosphorylation; D011506:Proteins / Q000145:classification*; D012660:Semantics; D018875:Vocabulary, Controlled
D011506:Proteins