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Article
Nature Genetics  34, 166 - 176 (2003)
Published online: 11 May 2003; | doi:10.1038/ng1165

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Eran Segal1, 6, Michael Shapira2, Aviv Regev3, 5, 6, Dana Pe'er4, 6, David Botstein2, Daphne Koller1 & Nir Friedman4

1  Computer Science Department, Stanford University, Stanford, California, 94305, USA.

2  Department of Genetics, Stanford University School of Medicine, Stanford, California, 94305, USA.

3  Department of Cell Research and Immunology, Tel Aviv University & Computer Science Department, Weizmann Institute, Israel.

4  School of Computer Science & Engineering, Hebrew University, Jerusalem, 91904, Israel.

5  Present address: Bauer Center for Genomics Research, Harvard University, Cambridge, Massachusetts, USA.

6  These authors contributed equally to this manuscript.

Correspondence should be addressed to Eran Segal eran@cs.stanford.edu or Daphne Koller koller@cs.stanford.edu
Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

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Nature Genetics
ISSN: 1061-4036
EISSN: 1546-1718
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