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Journal of Biomedicine and Biotechnology
Volume 2012 (2012), Article ID 245968, 8 pages
http://dx.doi.org/10.1155/2012/245968
Research Article

An Integrative Approach to Infer Regulation Programs in a Transcription Regulatory Module Network

1Freiburg Institute for Advanced Studies, University of Freiburg, Albertstraße 19, 79104 Freiburg im Breisgau, Germany
2Department of Computer Science and Software Engineering, Concordia University, 1455 de Maisonneuve Boulevard W, Montreal, QC, Canada H3G 1M8

Received 27 October 2011; Accepted 12 February 2012

Academic Editor: Yong Lim

Copyright © 2012 Jianlong Qi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The module network method, a special type of Bayesian network algorithms, has been proposed to infer transcription regulatory networks from gene expression data. In this method, a module represents a set of genes, which have similar expression profiles and are regulated by same transcription factors. The process of learning module networks consists of two steps: first clustering genes into modules and then inferring the regulation program (transcription factors) of each module. Many algorithms have been designed to infer the regulation program of a given gene module, and these algorithms show very different biases in detecting regulatory relationships. In this work, we explore the possibility of integrating results from different algorithms. The integration methods we select are union, intersection, and weighted rank aggregation. Experiments in a yeast dataset show that the union and weighted rank aggregation methods produce more accurate predictions than those given by individual algorithms, whereas the intersection method does not yield any improvement in the accuracy of predictions. In addition, somewhat surprisingly, the union method, which has a lower computational cost than rank aggregation, achieves comparable results as given by rank aggregation.