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Profile Context-Sensitive HMMs for Probabilistic Modeling of Sequences With Complex Correlations

Yoon, Byung-Jun and Vaidyanathan, P. P. (2006) Profile Context-Sensitive HMMs for Probabilistic Modeling of Sequences With Complex Correlations. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2006 (ICASSP 2006), Toulouse, France, 14-19 May 2006. Vol.3. IEEE , Piscataway, NJ, pp. 317-320. ISBN 142440469X.

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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.

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Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2006 IEEE. Reprinted with Permission. Publication Date: 14-19 May 2006. Posted online: 2006-07-24. Work supported in parts by the NSF grant CCF-0428326 and the Microsoft Research Graduate Fellowship.
Funding AgencyGrant Number
Microsoft Research Graduate FellowshipUNSPECIFIED
Subject 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
Record Number:CaltechAUTHORS:YOOicassp06
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Official Citation:Byung-Jun Yoon; Vaidyanathan, P.P.; , "Profile Context-Sensitive HMMs for Probabilistic Modeling of Sequences With Complex Correlations," Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on , vol.3, no., pp.III, 14-19 May 2006 doi: 10.1109/ICASSP.2006.1660654 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:9733
Deposited On:12 Mar 2008
Last Modified:08 Nov 2021 21:01

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