Prediction of protein complexes based on protein interaction data and functional annotation data using kernel methods

Authors:
Shi-Hua Zhang;Xue-Mei Ning;Hong-Wei Liu;Xiang-Sun Zhang
Affiliations:
Institute of Applied Mathematics, Academy of Mathematics and Systems Science Chinese Academy of Sciences, Beijing, China;Institute of Applied Mathematics, Academy of Mathematics and Systems Science Chinese Academy of Sciences, Beijing, China;School of Economics, Renmin University of China, Beijing, China;Institute of Applied Mathematics, Academy of Mathematics and Systems Science Chinese Academy of Sciences, Beijing, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

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Abstract

Prediction of protein complexes is a crucial problem in computational biology. The increasing amount of available genomic data can enhance the identification of protein complexes. Here we describe an approach for predicting protein complexes based on integration of protein-protein interaction (PPI) data and protein functional annotation data. The basic idea is that proteins in protein complexes often interact with each other and protein complexes exhibit high functional consistency/even multiple functional consistency. We create a protein-protein relationship network (PPRN) via a kernel-based integration of these two genomic data. Then we apply the MCODE algorithm on PPRN to detect network clusters as numerically determined protein complexes. We present the results of the approach to yeast Sacchromyces cerevisiae. Comparison with well-known experimentally derived complexes and results of other methods verifies the effectiveness of our approach.