Bayesian sparse hidden components analysis for transcription regulation networks | Zendy

Chiara Sabatti | Zendy; Gareth James | Zendy

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Bayesian sparse hidden components analysis for transcription regulation networks

Author(s) -

Chiara Sabatti,

Gareth James

Publication year - 2005

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/btk017

Subject(s) - computational biology , computer science , bayesian network , bayesian probability , bayes' theorem , expression (computer science) , set (abstract data type) , gene regulatory network , data mining , gene , transcription (linguistics) , biology , gene expression , machine learning , genetics , artificial intelligence , linguistics , philosophy , programming language

In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert.

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