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Recovering Genetic Regulatory Networks by Integrating Multiple Data Sources

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3 Author(s)
Wentao Zhao ; Texas A&M Univ., College Station ; Serpedin, E. ; Dougherty, E.R.

This paper proposes a novel algorithm for inferring genetic regulatory networks by exploiting the knowledge of gene expressions, DNA sequences and binding sites. The integration of multiple data sources helps to improve both the specificity and the sensitivity of network inference. The transcription factors of a target gene are determined by applying the reversible jump Markov chain Monte-Carlo (RJMCMC) algorithm to the linear regression model. The scheme is simulated on yeast data and the results provide insight on the regulation mechanism associated with environmental changes.

Published in:

Genomic Signal Processing and Statistics, 2007. GENSIPS 2007. IEEE International Workshop on

Date of Conference:

10-12 June 2007

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