Reverse-Engineering Transcriptional Modules from Gene Expression Data

Tom Michoel, Riet De Smet, Anagha Joshi, Kathleen Marchal, Yves Van de Peer

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

Abstract

"Module networks" are a framework to learn gene regulatory networks from expression data using a probabilistic model in which coregulated genes share the same parameters and conditional distributions. We present a method to infer ensembles of such networks and an averaging procedure to extract the statistically most significant modules and their regulators. We show that the inferred probabilistic models extend beyond the dataset used to learn the models.

Original languageEnglish
Title of host publicationCHALLENGES OF SYSTEMS BIOLOGY: COMMUNITY EFFORTS TO HARNESS BIOLOGICAL COMPLEXITY
EditorsG Stolovitzky, P Kahlem, A Califano
Place of PublicationOXFORD
PublisherBlackwell Publishing Ltd
Pages36-43
Number of pages8
Volume1158
ISBN (Print)978-1-57331-751-1
DOIs
Publication statusPublished - 2009
EventENFIN-DREAM Conference on the Assessment of Computational Methods in Systems Biology - Madrid
Duration: 28 Apr 200829 Apr 2008

Conference

ConferenceENFIN-DREAM Conference on the Assessment of Computational Methods in Systems Biology
CityMadrid
Period28/04/0829/04/08

Keywords

  • reverse engineering
  • transcriptional modules
  • probabillistic graphical models
  • ensemble methods

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