Recovering genetic regulatory networks by integrating multiple data sources

Wentao Zhao, Erchin Serpedin, Edward R. Dougherty

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 - Tuusula, Finland
Duration: 10 Jun 200712 Jun 2007

Other

Other5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
CountryFinland
CityTuusula
Period10/6/0712/6/07

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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