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 language | English |
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Journal | Computer Applications in the Biosciences |
Volume | 12 |
Pages (from-to) | 95-107 |
ISSN | 0266-7061 |
Publication status | Published - 1996 |