Detection of Gene Interactions Based on Syntactic Relations. (16th March 2008)

• Record Type: Journal Article
• Authors: Kim, Mi-Young
• Other Names: Howard Daniel Academic Editor.
• Is Part Of: Journal of biomedicine and biotechnology. Volume 2008(2008)
• Publication Date: 2008-03-16
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Medicine; Biology; Biotechnology; Médecine; Biologie; Biotechnologie; Biology; Biotechnology; Medicine; Biotechnology; Biomedicine; Electronic journals; Periodical; Periodicals; Electronic journals; 610
• DOI: 10.1155/2008/371710
• Languages: English
• ISSNs: 1110-7243

Abstract

Abstract : Interactions between proteins and genes are considered essential in the description of biomolecular phenomena, and networks of interactions are applied in a system's biology approach. Recently, many studies have sought to extract information from biomolecular text using natural language processing technology. Previous studies have asserted that linguistic information is useful for improving the detection of gene interactions. In particular, syntactic relations among linguistic information are good for detecting gene interactions. However, previous systems give a reasonably good precision but poor recall. To improve recall without sacrificing precision, this paper proposes a three-phase method for detecting gene interactions based on syntactic relations. In the first phase, we retrieve syntactic encapsulation categories for each candidate agent and target. In the second phase, we construct a verb list that indicates the nature of the interaction between pairs of genes. In the last phase, we determine direction rules to detect which of two genes is the agent or target. Even without biomolecular knowledge, our method performs reasonably well using a small training dataset. While the first phase contributes to improve recall, the second and third phases contribute to improve precision. In the experimental results using ICML 05 Workshop on Learning Language in Logic (LLL05) data, our proposed method gave an F-measure of 67.2% for the test data, significantly outperforming previous methods. We also describe the contribution of each phase to the performance.