Title: Protein function prediction with the shortest path in functional linkage graph and boosting

Authors: Xing-Ming Zhao, Luonan Chen, Kazuyuki Aihara

Addresses: Institute of Systems Biology, Shanghai University, Shanghai 200444, China; ERATO Aihara Complexity Modelling Project, JST, Tokyo 151-0064, Japan; Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, 230031, China; Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan. ' Institute of Systems Biology, Shanghai University, Shanghai 200444, China; ERATO Aihara Complexity Modelling Project, JST, Tokyo, 151-0064, Japan; Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan; Department of Electrical Engineering and Electronics, Osaka Sangyo University, Osaka, Japan. ' ERATO Aihara Complexity Modelling Project, JST, Tokyo, 151-0064, Japan; Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan

Abstract: Annotating proteins with biological functions is one of the main goals in post genomic era. Various high-throughout technologies, e.g., yeast two-hybrid systems and microarray, have provided an alternative way to protein function prediction. Despite the success obtained by high-throughout data, the errors in the data have not been handled well. In this work, a new technique for protein function prediction is presented, where a weighted functional linkage graph is generated by exploiting the existing protein-protein interaction data, complex data and gene expression data. By finding the shortest path in the functional linkage graph, the functional links among proteins can be captured. With the functional links available, the functions of unknown proteins can be predicted utilising support vector machines and the functions of those proteins that have functional links to the unknown proteins. In addition, the boosting algorithm is employed to further improve the prediction accuracy. The experiments on yeast genes show promising results and prove the efficiency of the proposed methods.

Keywords: protein function prediction; shortest path; functional linkage graph; boosting algorithms; support vector machines; SVM; bioinformatics; protein-protein interaction; complex data; gene expression data; yeast genes.

DOI: 10.1504/IJBRA.2008.021175

International Journal of Bioinformatics Research and Applications, 2008 Vol.4 No.4, pp.375 - 384

Published online: 08 Nov 2008 *

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