Recovering genetic regulatory networks by integrating multiple data sources

Wentao Zhao; Erchin Serpedin; Edward R. Dougherty

doi:10.1109/GENSIPS.2007.4365813

Recovering genetic regulatory networks by integrating multiple data sources

Wentao Zhao, Erchin Serpedin, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
DOIs	https://doi.org/10.1109/GENSIPS.2007.4365813
Publication status	Published - 2007
Externally published	Yes
Event	5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 - Tuusula, Finland Duration: 10 Jun 2007 → 12 Jun 2007

Other

Other	5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
Country	Finland
City	Tuusula
Period	10/6/07 → 12/6/07

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ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Computer Science Applications
Signal Processing
Electrical and Electronic Engineering

Cite this

Zhao, W., Serpedin, E., & Dougherty, E. R. (2007). Recovering genetic regulatory networks by integrating multiple data sources. In 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 [4365813] https://doi.org/10.1109/GENSIPS.2007.4365813

Zhao, W, Serpedin, E & Dougherty, ER 2007, Recovering genetic regulatory networks by integrating multiple data sources. in 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07., 4365813, 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07, Tuusula, Finland, 10/6/07. https://doi.org/10.1109/GENSIPS.2007.4365813

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AB - 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.

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Access to Document

10.1109/GENSIPS.2007.4365813