Profile context-sensitive HMMS for probabilistic modeling of sequences with complex correlations Conference Paper uri icon

abstract

  • The profile hidden Markov model is a specific type of HMM that is well suited for describing the common features of a set of related sequences. It has been extensively used in computational biology, where it is still one of the most popular tools. In this paper, we propose a new model called the profile context-sensitive HMM. Unlike traditional profile-HMMs, the proposed model is capable of describing complex long-range correlations between distant symbols in a consensus sequence. We also introduce a general algorithm that can be used for finding the optimal state-sequence of an observed symbol sequence based on the given profile-csHMM. The proposed model has an important application in RNA sequence analysis, especially in modeling and analyzing RNA pseudoknots. © 2006 IEEE.

author list (cited authors)

  • Yoon, B. J., & Vaidyanathan, P. P.

publication date

  • December 2006