Recovering Genetic Regulatory Networks by Integrating Multiple Data Sources
Conference Paper
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Overview
abstract
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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
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Fifth IEEE International Workshop on Genomic Signal Processing and Statistics
published proceedings
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2007 IEEE International Workshop on Genomic Signal Processing and Statistics
author list (cited authors)
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Zhao, W., Serpedin, E., & Dougherty, E. R
citation count
complete list of authors
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Zhao, Wentao||Serpedin, Erchin||Dougherty, Edward R
publication date
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Research
keywords
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Generic Health Relevance
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Genetics
Identity
Digital Object Identifier (DOI)
International Standard Book Number (ISBN) 10
International Standard Book Number (ISBN) 13
Additional Document Info