Prediction of regulatory networks: genome-wide identification of transcription factor targets from gene expression data
Author(s) -
Jiang Qian,
Jimmy Lin,
Nicholas M. Luscombe,
Haiyuan Yu,
Mark Gerstein
Publication year - 2003
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/btg347
Subject(s) - computational biology , transcription factor , false positive paradox , biology , genome , gene regulatory network , data mining , support vector machine , computer science , dna microarray , cluster analysis , gene , genetics , gene expression , machine learning
Defining regulatory networks, linking transcription factors (TFs) to their targets, is a central problem in post-genomic biology. One might imagine one could readily determine these networks through inspection of gene expression data. However, the relationship between the expression timecourse of a transcription factor and its target is not obvious (e.g. simple correlation over the timecourse), and current analysis methods, such as hierarchical clustering, have not been very successful in deciphering them.
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