Multiple Sequence Alignment by Improved Hidden Markov Model Training and Quantum-Behaved Particle Swarm Optimization

被引：5

作者：

Li, Chengyuan ^{[1
]}

Long, Haixia ^{[2
]}

Ding, Yanrui ^{[1
]}

Sun, Jun ^{[1
]}

Xu, Wenbo ^{[1
]}

机构：

[1] Jiangnan Univ, Sch IOT Engn, Lihu Rd 1800, Wuxi 214122, Jiangsu, Peoples R China

[2] Jiangnan Univ, Sch Educ, Wuxi 214122, Peoples R China

来源：

LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING | 2010年 / 6330卷

关键词：

multiple sequence alignment; Profile hidden Markov model; Quantum-behaved particle swarm optimization;

D O I：

10.1007/978-3-642-15615-1_43

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

Multiple sequence alignment (MSA), known as NP-complete problem, is one of the basic problems in computational biology. Presently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization (QPSO) is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and QPSO for the MSA is proposed. In order to evaluate the approach protein sequences are taken. Finally, compared with other algorithms, the results show that the proposed algorithm not only finds out perfect profile HMM, but also produces the optimal alignment of multiple sequences.

引用

页码：358 / +

页数：3

共 22 条

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