Mohammad Soruri - Department of Electrical and Computer Engineering,University of BirjandBirjand, Iran
Javad Sadri - Department of Electrical and Computer Engineering,University of BirjandBirjand, Iran
S. Hamid Zahiri - Department of Electrical and Computer Engineering University of Birjand Birjand, Iran

Sequence Modeling is one of the most important problems in bioinformatics. In the sequential data modeling, Hidden Markov Models(HMMs) have been widely used to findsimilarity between sequences, since the performance of HMMs are suitable for handling of sequence patterns with variouslengths. In this paper, a new approach for biological sequencemodeling scheme based on HMMs optimized by Particle Swarm Optimization(PSO) algorithm is introduced. In this approach,each sequence is described by a specific HMM, and then for each model, its probability to generate individual sequence isevaluated. Then, the generated sequence is compared with actual sequence. Experiments carried out on gene sequences dataset show that the proposed approach can be successfully utilized for sequence modeling.

Baum-Welch Algorithm; Hidden Markov Model (HMM); Particle Swarm Optimization (PSO); Sequence Modeling