REGNET: mining context-specific human transcription networks using composite genomic information
2014
Background
Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.
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