Recovering Genetic Regulatory Networks by Integrating Multiple Data Sources Conference Paper

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. © 2007 IEEE.

name of conference

• Fifth IEEE International Workshop on Genomic Signal Processing and Statistics

authors

• Dougherty, Edward
• Serpedin, Erchin

published proceedings

• 2007 IEEE International Workshop on Genomic Signal Processing and Statistics

author list (cited authors)

• Zhao, W., Serpedin, E., & Dougherty, E. R

citation count

• 0

complete list of authors

• Zhao, Wentao||Serpedin, Erchin||Dougherty, Edward R

publication date

• June 2007

publisher

• IEEE  Publisher

published in

• 2007 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS  Journal

keywords

• Generic Health Relevance
• Genetics

Digital Object Identifier (DOI)

• 10.1109/gensips.2007.4365813

International Standard Book Number (ISBN) 10

• 1424409993

International Standard Book Number (ISBN) 13

• 978-1-4244-0998-3

start page

• 1

end page

• 2