Bayesian detection of non-sinusoidal periodic patterns in circadian expression data | Zendy

Darya Chudova | Zendy; Alexander Ihler | Zendy; Kevin Lin | Zendy; Bogi Andersen | Zendy; Padhraic Smyth | Zendy

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Bayesian detection of non-sinusoidal periodic patterns in circadian expression data

Author(s) -

Darya Chudova,

Alexander Ihler,

Kevin Lin,

Bogi Andersen,

Padhraic Smyth

Publication year - 2009

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/btp547

Subject(s) - circadian rhythm , bayesian probability , expression (computer science) , computer science , pattern recognition (psychology) , statistics , algorithm , computational biology , data mining , artificial intelligence , biology , mathematics , neuroscience , programming language

Cyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns.

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