Title: Large-scale Protein-Protein Interaction prediction using novel kernel methods

Authors: Xue-wen Chen, Bing Han, Jianwen Fang, Ryan J. Haasl

Addresses: Electrical Engineering and Computer Science Department, The University of Kansas, Lawrence, KS 66045, USA. ' Electrical Engineering and Computer Science Department, The University of Kansas, Lawrence, KS 66045, USA. ' Bioinformatics Core Facility, University of Kansas, Lawrence, KS 66047, USA. ' Bioinformatics Core Facility, University of Kansas, Lawrence, KS 66047, USA

Abstract: Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.

Keywords: kernel method; support vector machine; SVM; protein-protein interactions; PPIs; data mining; bioinformatics; molecular mechanisms; cell properties; PPI prediction; feature representation.

DOI: 10.1504/IJDMB.2008.019095

International Journal of Data Mining and Bioinformatics, 2008 Vol.2 No.2, pp.145 - 156

Published online: 28 Jun 2008 *

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