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

被引：0
|
作者：
Ge, HW [1 ]
Liang, YC [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
来源：
AI 2005: ADVANCES IN ARTIFICIAL INTELLIGENCE | 2005年 / 3809卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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, BAHBASE. 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.
引用
收藏
页码：756 / 765
页数：10
相关论文
共 50 条
  • [1] Improved Hidden Markov Model training for multiple sequence alignment by a particle swarm optimization - evolutionary algorithm hybrid
    Rasmussen, TK
    Krink, T
    BIOSYSTEMS, 2003, 72 (1-2) : 5 - 17
  • [2] ProbPFP: A Multiple Sequence Alignment Algorithm Combining Partition Function and Hidden Markov Model with Particle Swarm Optimization
    Zhan, Qing
    Wang, Nan
    Jin, Shuilin
    Tan, Renjie
    Jiang, Qinghua
    Wang, Yadong
    PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2018, : 1290 - 1295
  • [3] ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function
    Zhan, Qing
    Wang, Nan
    Jin, Shuilin
    Tan, Renjie
    Jiang, Qinghua
    Wang, Yadong
    BMC BIOINFORMATICS, 2019, 20 (Suppl 18)
  • [4] ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function
    Qing Zhan
    Nan Wang
    Shuilin Jin
    Renjie Tan
    Qinghua Jiang
    Yadong Wang
    BMC Bioinformatics, 20
  • [5] Multiple Sequence Alignment by Improved Hidden Markov Model Training and Quantum-Behaved Particle Swarm Optimization
    Li, Chengyuan
    Long, Haixia
    Ding, Yanrui
    Sun, Jun
    Xu, Wenbo
    LIFE SYSTEM MODELING AND INTELLIGENT COMPUTING, 2010, 6330 : 358 - +
  • [6] Multiple Sequence Alignment with Hidden Markov Models Learned by Random Drift Particle Swarm Optimization
    Sun, Jun
    Palade, Vasile
    Wu, Xiaojun
    Fang, Wei
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2014, 11 (01) : 243 - 257
  • [7] Multiple sequence alignment based on Profile hidden Markov model and quantum-behaved particle swarm optimization with selection method
    Long, HaiXia
    Wu, LiHua
    Zhang, Yu
    ADVANCED RESEARCH ON MATERIAL ENGINEERING, CHEMISTRY AND BIOINFORMATICS, PTS 1 AND 2 (MECB 2011), 2011, 282-283 : 7 - 12
  • [8] A hybrid algorithm based on artificial immune system and hidden Markov model for multiple sequence alignment
    Ge, Hongwei
    Zhong, Weimin
    Du, Wenli
    Qian, Feng
    Wang, Lu
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007), 2007,
  • [9] Multiple sequence alignment using the Hidden Markov Model trained by an improved quantum-behaved particle swarm optimization
    Sun, Jun
    Wu, Xiaojun
    Fang, Wei
    Ding, Yangrui
    Long, Haixia
    Xu, Webo
    INFORMATION SCIENCES, 2012, 182 (01) : 93 - 114
  • [10] A multiple sequence alignment algorithm based on inertia weights particle swarm optimization
    Gao, Yuxi
    Journal of Bionanoscience, 2014, 8 (05): : 400 - 404
12345