Inferring transcriptional regulatory networks from high-throughput data | Zendy

RuiSheng Wang | Zendy; Yong Wang | Zendy; Yu Xia | Zendy; Luonan Chen | Zendy

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Inferring transcriptional regulatory networks from high-throughput data

Author(s) -

RuiSheng Wang,

Yong Wang,

Yu Xia,

Luonan Chen

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm465

Subject(s) - computer science , inference , cis regulatory module , transcription factor , computational biology , data mining , transcription (linguistics) , gene regulatory network , dna microarray , transcriptional regulation , software , gene , biology , gene expression , genetics , artificial intelligence , linguistics , philosophy , enhancer , programming language

Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various post-translational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes.

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