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

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.

authors

• Yoon, Byung-Jun

published proceedings

• ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

author list (cited authors)

• Yoon, B. J., & Vaidyanathan, P. P.

complete list of authors

• Yoon, BJ||Vaidyanathan, PP

publication date

• December 2006

published in

• Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing / sponsored by the Institute of Electrical and Electronics Engineers Signal Processing Society. ICASSP (Conference)  Journal

International Standard Book Number (ISBN) 10

• 142440469X

International Standard Book Number (ISBN) 13

• 9781424404698

volume

• 3