Inferring Subnetworks from Perturbed Expression Profiles
D. Pe'er, A. Regev, G. Elidan, and N. Friedman
In 9th Inter. Conf. on Intelligent Systems for Molecular Biology (ISMB), 2001.
Genome-wide expression profiles of genetic mutants provide a wide
variety of measurements of cellular responses to perturbations.
Typical analysis of such data identifies genes affected by
perturbation and uses clustering to group genes of similar function.
In this paper we discover a finer structure of interactions between
genes, such as causality, mediation, activation, and inhibition by
using a Bayesian network framework. We extend this framework to
correctly handle perturbations, and to identify significant
substructures of interacting genes. We apply this method to
expression data of S. cerevisiae mutants and uncover a
variety of structured metabolic, signaling and regulatory pathways.
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