Algorithms for hidden markov models restricted to occurrences of regular expressions.

ABSTRACT: Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurre

SUBMITTER: Tataru P 

PROVIDER: S-EPMC4009796 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature