Hidden Markov Model Optimized by PSO Algorithm for Gene Sequence Clustering

Gene sequence modeling and clustering is one of the most important problems in bioinformatics. Hidden Markov Models (HMMs) have been widely used to find similarity between sequences with large and various lengths. In this paper a novel gene sequence clustering method based on HMMs optimized by Particle Swarm Optimization (PSO) algorithm is introduced. In this approach, each gene sequence is described by a specific HMM, and then its probability to generate individual sequence is evaluated for each model. A hierarchical clustering algorithm based on a new definition of a distance measure, has been applied to find the best clusters. Experiments carried out on lung cancer related genes dataset show that the proposed approach can be successfully utilized for gene clustering.