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Bioinformatics Advance Access published online on March 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti406
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received November 12, 2004
Revised March 2, 2005
Accepted March 22, 2005

Article

Modularized learning of genetic interaction networks from biological annotations and mRNA expression data

Phil Hyoun Lee 1 and Doheon Lee 2*

1 School of Computing, Queen's University, Canada
2 Department of BioSystems, KAIST, Korea

* To whom correspondence should be addressed.
Doheon Lee, E-mail: doheon{at}kaist.ac.kr


  Abstract

Motivation: Inferring genetic interaction mechanism using Bayesian networks has recently drawn increasing attention due to its well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences.

Results: We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method Modularized Network Learning (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity, and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze S. cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches.

Availability: JAVA programs for the MONET framework is available from the corresponding author upon request. Web supplementary data is accessible at http://biosoft.kaist.ac.kr/~dhlee/monet/index.html.


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