REGNET: mining context-specific human transcription networks using composite genomic information.

Citation data:

BMC genomics, ISSN: 1471-2164, Vol: 15, Issue: 1, Page: 450

Publication Year:
2014
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Repository URL:
http://scholarworks.unist.ac.kr/handle/201301/5110
PMID:
24912499
DOI:
10.1186/1471-2164-15-450
PMCID:
PMC4070555
Author(s):
Chi, Sang-Mun; Seo, Young-Kyo; Park, Young-Kyu; Yoon, Sora; Park, Chan Young; Kim, Yong Sung; Kim, Seon-Young; Nam, Dougu
Publisher(s):
Springer Nature; BIOMED CENTRAL LTD
Tags:
Biochemistry, Genetics and Molecular Biology; Composite gene-set analysis; Gene Ontology; KEGG; Microarray; TFBS; Transcription network
article description
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.