meta-MEME: Motif-based hidden Markov models of protein families | Zendy

William Noble Grundy | Zendy; Timothy L. Bailey | Zendy; Charles Elkan | Zendy; Michael E. Baker | Zendy

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meta-MEME: Motif-based hidden Markov models of protein families

Author(s) -

William Noble Grundy,

Timothy L. Bailey,

Charles Elkan,

Michael E. Baker

Publication year - 1997

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/13.4.397

Subject(s) - hidden markov model , motif (music) , computer science , markov chain , artificial intelligence , pattern recognition (psychology) , natural language processing , machine learning , physics , acoustics

Modeling families of related biological sequences using Hidden Markov models (HMMs), although increasingly widespread, faces at least one major problem: because of the complexity of these mathematical models, they require a relatively large training set in order to accurately recognize a given family. For families in which there are few known sequences, a standard linear HMM contains too many parameters to be trained adequately.

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