PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Protein functional class prediction with a combined graph
Hyunjung Shin, Koji Tsuda and Bernhard Schölkopf
Expert Systems with Applications Volume 36, pp. 3284-3292, 2009.

Abstract

In bioinformatics, there exist multiple descriptions of graphs for the same set of genes or proteins. For instance, in yeast systems, graph edges can represent different relationships such as protein–protein interactions, genetic interactions, or co-participation in a protein complex, etc. Relying on similarities between nodes, each graph can be used independently for prediction of protein function. However, since different graphs contain partly independent and partly complementary information about the problem at hand, one can enhance the total information extracted by combining all graphs. In this paper, we propose a method for integrating multiple graphs within a framework of semi-supervised learning. The method alternates between minimizing the objective function with respect to network output and with respect to combining weights. We apply the method to the task of protein functional class prediction in yeast. The proposed method performs significantly better than the same algorithm trained on any single graph.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4403
Deposited By:Koji Tsuda
Deposited On:13 March 2009