Hidden Markov models for sequence analysis: extension and analysis of the basic method

Richard Hughey, Anders Stærmose Krogh

    Research output: Contribution to journalJournal articlepeer-review

    Abstract

    Hidden Markov models (HMMs) are a highly effective means of modeling a family of unalignedsequences or a common motif within a set of unaligned sequences. The trained HMM can then beused for discrimination or multiple alignment. The basic mathematical description of an HMMand its expectation-maximization training procedure is relatively straight-forward. In this paper,we review the mathematical extensions and heuristics that move the method from the theoreticalto the practical. Then, we experimentally analyze the effectiveness of model regularization,dynamic model modification, and optimization strategies. Finally it is demonstrated on the SH2domain how a domain can be found from unaligned sequences using a special model type. Theexperimental work was completed with the aid of the Sequence Alignment and Modeling softwaresuite.
    Original languageEnglish
    JournalCABIOS
    Volume12
    Pages (from-to)95-107
    ISSN0266-7061
    Publication statusPublished - 1996

    Fingerprint

    Dive into the research topics of 'Hidden Markov models for sequence analysis: extension and analysis of the basic method'. Together they form a unique fingerprint.

    Cite this