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Protein-Protein Interaction Prediction using Interaction Significance Matrix  

Jang, Woo-Hyuk (KAIST 정보통신공학과)
Jung, Suk-Hoon (KAIST 정보통신공학과)
Jung, Hwie-Sung (KAIST 정보통신공학과)
Hyun, Bo-Ra (KAIST 정보통신공학과)
Han, Dong-Soo (KAIST 전산학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.10, 2009 , pp. 851-860 More about this Journal
Abstract
Recently, among the computational methods of protein-protein interaction prediction, vast amounts of domain based methods originated from domain-domain relation consideration have been developed. However, it is true that multi domains collaboration is avowedly ignored because of computational complexity. In this paper, we implemented a protein interaction prediction system based the Interaction Significance matrix, which quantified an influence of domain combination pair on a protein interaction. Unlike conventional domain combination methods, IS matrix contains weighted domain combinations and domain combination pair power, which mean possibilities of domain collaboration and being the main body on a protein interaction. About 63% of sensitivity and 94% of specificity were measured when we use interaction data from DIP, IntAct and Pfam-A as a domain database. In addition, prediction accuracy gradually increased by growth of learning set size, The prediction software and learning data are currently available on the web site.
Keywords
domain combination pair; weighted domain combination; domain combination pair power; protein interaction prediction; possibility of domain collaboration;
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