Display Settings:

Format

Send to:

Choose Destination
Biosystems. 2011 Mar;103(3):425-34. doi: 10.1016/j.biosystems.2010.12.004. Epub 2010 Dec 17.

A multiorganism based method for Bayesian gene network estimation.

Author information

  • 1Department of Electrical and Computer Engineering, American University of Beirut, Riad El-Solh, Lebanon. zd03@aub.edu.lb

Abstract

The primary goal of this article is to infer genetic interactions based on gene expression data. A new method for multiorganism Bayesian gene network estimation is presented based on multitask learning. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from true correlations that would lead to actual edges when modeling the gene interactions as a Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes.

Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

PMID:
21168470
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for Elsevier Science
    Loading ...
    Write to the Help Desk