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The Scientific World Journal
Volume 2014, Article ID 240673, 7 pages
Research Article

Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

1Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan
2Japan Ichiba Section Development Unit, Rakuten Inc., 4-12-3 Higashi-shinagawa, Shinagawa-ku, Tokyo 140-0002, Japan
3Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan

Received 3 April 2014; Accepted 30 May 2014; Published 24 June 2014

Academic Editor: Loris Nanni

Copyright © 2014 Mayumi Kamada et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.