Title :
Profile Context-Sensitive HMMs for Probabilistic Modeling of Sequences With Complex Correlations
Author :
Yoon, Byung-Jun ; Vaidyanathan, P.P.
Author_Institution :
Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA
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
Keywords :
hidden Markov models; macromolecules; molecular biophysics; sequences; RNA pseudoknots; RNA sequence analysis; complex correlation sequences; complex long-range correlations; computational biology; probabilistic modeling; profile context-sensitive HMM; profile hidden Markov model; state-sequence; symbol sequence; Biological system modeling; Computational biology; Context modeling; Databases; Electronic mail; Frequency; Hidden Markov models; Proteins; RNA; Sequences;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1660654
