Eran Segal 1, E, Michael Shapira 2, Aviv Regev 3, 5, E, Dana Pe'er 4, E, David Botstein 2, Daphne Koller 1 & Nir Friedman 4
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.
E These authors contributed equally to this manuscript.
Correspondence should be addressed to:
E Segal. e-mail: eran@cs.stanford.edu
and D Koller. e-mail: 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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