A hidden markov model and immune particle swarm optimization-based algorithm for multiple sequence alignment

Authors:
Hong-Wei Ge;Yan-Chun Liang
Affiliations:
College of Computer Science, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 2
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Abstract

Multiple sequence alignment (MSA) is a fundamental and challenging problem in the analysis of biologic sequences. In this paper, an immune particle swarm optimization (IPSO) is proposed, which is based on the models of the vaccination and the receptor editing in immune systems. The proposed algorithm is used to train hidden Markov models (HMMs), further, an integration algorithm based on the HMM and IPSO for the MSA is constructed. The approach is tested on a set of standard instances taken from the Benchmark Alignment database, BAliBASE. Numerical simulated results are compared with those obtained by using the Baum-Welch training algorithm. The results show that the proposed algorithm not only improves the alignment abilities, but also reduces the time cost.