Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data.

ABSTRACT: The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such 'reverse-engineering' methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)-target gene expression levels and estimating TFs' activ

SUBMITTER: Wu M 

PROVIDER: S-EPMC3294238 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature