Protein association discovery in biomedical literature

Author

Fu, Yueyu ; Mostafa, Javed ; Seki, Kazuhiro

Author_Institution

Lab. of Appl. Informatics Res., Indiana Univ., Bloomington, IN, USA

fYear

2003

fDate

27-31 May 2003

Firstpage

113

Lastpage

115

Abstract

Protein association discovery can directly contribute toward developing protein pathways; hence it is a significant problem in bioinformatics. LUCAS (Library of User-Oriented Concepts for Access Services) was designed to automatically extract and determine associations among proteins from biomedical literature. Such a tool has notable potential to automate database construction in biomedicine, instead of relying on experts´ analysis. We report on the mechanisms for automatically generating clusters of proteins. A formal evaluation of the system, based on a subset of 2000 MEDLINE titles and abstracts, has been conducted against Swiss-Prot database in which the associations among concepts are entered by experts manually.

Keywords

Internet; biocomputing; digital libraries; information retrieval; natural languages; pattern clustering; proteins; unsupervised learning; user interfaces; very large databases; LUCAS system; Library of User-Oriented Concepts for Access Service; MEDLINE database; Swiss-Prot database; automated database construction; bioinformatics; biomedical literature; formal evaluation; information retrieval process; natural language processing technique; protein association discovery; protein cluster; statistical technique; Bioinformatics; Biomedical informatics; Clustering algorithms; Data mining; Databases; Iterative algorithms; Laboratories; Libraries; Matrix decomposition; Protein engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Libraries, 2003. Proceedings. 2003 Joint Conference on

Print_ISBN

0-7695-1939-3

Type

conf

DOI

10.1109/JCDL.2003.1204848

Filename

1204848