Combining Evidence, Specificity, and Proximity towards the Normalization of Gene Ontology Terms in Text. (15th April 2008)

• Record Type: Journal Article
• Authors: Gaudan, S.; Jimeno Yepes, A.; Lee, V.; Rebholz-Schuhmann, D.
• Other Names: Robin Stéphane Academic Editor.
• Is Part Of: EURASIP journal on bioinformatics and systems biology. Volume 2008(2008)
• Publication Date: 2008-04-15
• Subjects: Bioinformatics -- Periodicals; Systems biology -- Periodicals; Systems Biology; Signal Processing, Computer-Assisted; Bio-informatique; Biologie systémique; Bioinformatics; Systems biology; Systems Biology; Bioinformatics; Electronic journals; Periodical; Fulltext; Internet Resources; Periodicals; Periodicals; 570.285
• DOI: 10.1155/2008/342746
• Languages: English
• ISSNs: 1687-4145

Abstract

Abstract : Structured information provided by manual annotation of proteins with Gene Ontology concepts represents a high-quality reliable data source for the research community. However, a limited scope of proteins is annotated due to the amount of human resources required to fully annotate each individual gene product from the literature. We introduce a novel method for automatic identification of GO terms in natural language text. The method takes into consideration several features: (1) the evidence for a GO term given by the words occurring in text, (2) the proximity between the words, and (3) the specificity of the GO terms based on their information content. The method has been evaluated on the BioCreAtIvE corpus and has been compared to current state of the art methods. The precision reached 0.34 at a recall of 0.34 for the identified terms at rank 1. In our analysis, we observe that the identification of GO terms in the "cellular component" subbranch of GO is more accurate than for terms from the other two subbranches. This observation is explained by the average number of words forming the terminology over the different subbranches.