Applying Linear Models to Learn Regulation Programs in a Transcription Regulatory Module Network

Jianlong Qi, Tom Michoel, Gregory Butler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationEVOLUTIONARY COMPUTATION, MACHINE LEARNING AND DATA MINING IN BIOINFORMATICS
Subtitle of host publicationLecture Notes in Computer Science
EditorsC Pizzuti, MD Ritchie, M Giacobini
Place of PublicationBERLIN
PublisherSpringer-Verlag GmbH
Pages37-47
Number of pages11
Volume6623
ISBN (Print)978-3-642-20388-6
Publication statusPublished - 2011
Event9th European Conference on Evolutionary Computation - Torino
Duration: 27 Apr 201129 Apr 2011

Conference

Conference9th European Conference on Evolutionary Computation
CityTorino
Period27/04/1129/04/11

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