Applying linear models to learn regulation programs in a transcription regulatory module network

Authors:
Jianlong Qi;Tom Michoel;Gregory Butler
Affiliations:
Department of Computer Science, Concordia University, Montreal, Quebec, Canada;Freiburg Institute for Advanced Studies, School of Life Sciences - LifeNet, Freiburg, Germany;Department of Computer Science, Concordia University, Montreal, Quebec, Canada
Venue:
EvoBIO'11 Proceedings of the 9th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2011

Citing 3
Cited 1

Learning Module Networks

The Journal of Machine Learning Research
Analysis of a Gibbs sampler method for model-based clustering of gene expression data

Bioinformatics
Module networks revisited

Bioinformatics

An integrative approach to infer regulation programs in a transcription regulatory module network

Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Abstract

The module network method has been widely used to infer transcriptional regulatory network from gene expression data. A common strategy of module network learning algorithms is to apply regression trees to infer the regulation program of a module. In this work we propose to apply linear models to fulfill this task. The novelty of our method is to extract the contrast in which a module's genes are most significantly differentially expressed. Consequently, the process of learning the regulation program for the module becomes one of identifying transcription factors that are also differentially expressed in this contrast. The effectiveness of our algorithm is demonstrated by the experiments in a yeast benchmark dataset.