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Statistical inference of transcriptional module-based gene networks from time course gene expression profiles by using state space models
Author(s) -
Osamu Hirose,
Ryo Yoshida,
Seiya Imoto,
Rui Yamaguchi,
Tomoyuki Higuchi,
D. Stephen CharnockJones,
Cristin G. Print,
Satoru Miyano
Publication year - 2008
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/btm639
Subject(s) - inference , gene , gene expression , computational biology , computer science , state (computer science) , state space , statistical inference , gene regulatory network , course (navigation) , regulation of gene expression , expression (computer science) , biology , artificial intelligence , genetics , algorithm , mathematics , programming language , statistics , engineering , aerospace engineering
Statistical inference of gene networks by using time-course microarray gene expression profiles is an essential step towards understanding the temporal structure of gene regulatory mechanisms. Unfortunately, most of the current studies have been limited to analysing a small number of genes because the length of time-course gene expression profiles is fairly short. One promising approach to overcome such a limitation is to infer gene networks by exploring the potential transcriptional modules which are sets of genes sharing a common function or involved in the same pathway.

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