Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions
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概要
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Identification of protein complexes is very useful because many proteins express their functional activity by interacting with other proteins and forming protein complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this technical report, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.
- 2013-12-04
著者
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Tatsuya Akutsu
Bioinformatics Center, Institute for Chemical Research, Kyoto University
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Morihiro Hayashida
Bioinformatics Center Institute For Chemical Research Kyoto University
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Peiying Ruan
Bioinformatics Center Institute For Chemical Research Kyoto University
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Tatsuya Akutsu
Bioinformatics Center Institute For Chemical Research Kyoto University
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Tatsuya Akutsu
Bioinformatics Center Institute For Chemical Research Kyoto Univerty
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Osamu Maruyama
Institute of Mathematics for Industry, Kyushu University
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