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

Richard Hugheygif and Anders Kroghgif

CABIOS 12(2):95-107, 1996

Running title: Hidden Markov models for sequence analysis

Keywords: Hidden Markov model, parallel computation, multiple sequence alignment, protein modeling, motif modeling.


Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and 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 theoretical to the practical. Then, we experimentally analyze the effectiveness of model regularization, dynamic model modification, and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite.

next up previous
Next: Introduction Up: Hidden Markov models for

Rey Rivera
Thu Aug 29 15:28:54 PDT 1996