Modeling promoter grammars with evolving hidden Markov models | Zendy

KyoungJae Won | Zendy; Albin Sandelin | Zendy; Troels Marstrand | Zendy; Anders Krogh | Zendy

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Modeling promoter grammars with evolving hidden Markov models

Author(s) -

KyoungJae Won,

Albin Sandelin,

Troels Marstrand,

Anders Krogh

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/btn254

Subject(s) - overfitting , computer science , hidden markov model , promoter , dna binding site , set (abstract data type) , rule based machine translation , artificial intelligence , machine learning , computational biology , gene , biology , genetics , artificial neural network , programming language , gene expression

Describing and modeling biological features of eukaryotic promoters remains an important and challenging problem within computational biology. The promoters of higher eukaryotes in particular display a wide variation in regulatory features, which are difficult to model. Often several factors are involved in the regulation of a set of co-regulated genes. If so, promoters can be modeled with connected regulatory features, where the network of connections is characteristic for a particular mode of regulation.

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