| Paper: | SPTM-P3.3 | ||
| Session: | System Modeling, Representation and Identification | ||
| Time: | Tuesday, May 16, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: System Modeling, Representation, and Identification | ||
| Title: | Profile Context-Sensitive HMMs for Probabilistic Modeling of Sequences with Complex Correlations | ||
| Authors: | Byung-Jun Yoon, P. P. Vaidyanathan, California Institute of Technology, United States | ||
| 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. | ||
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