チョウ ゼンシュン   Zhang Shanjun
  張 善俊
   所属   神奈川大学  情報学部 計算機科学科
    神奈川大学大学院  理学研究科 理学専攻(情報科学領域)
   職種   教授
言語種別 日本語
発行・発表の年月 2009/11
形態種別 その他
査読 査読あり
標題 Protein Structure Prediction with EPSO in Toy Model
執筆形態 共著
掲載誌名 IEEE Computer Society Conference Publications
巻・号・頁 2009 Second International Conference on Intelligent Networks and Intelligent Systems,,673-676頁
著者・共著者 Hongbing Zhu, Chengdong Pu, Xiaoli Lin, Jinguang Gu, Shanjun Zhang, and Mengsi Su
概要 Predicting the structure of protein through its
sequence of amino acids is a complex and challenging problem
in computational biology. Though toy model is one of the
simplest and effective models, it is still extremely difficult to
predict its structure as the increase of amino acids. Particle
swarm optimization (PSO) is a swarm intelligence algorithm,
has been successfully applied to many optimization problems
and shown its high search speed in these applications. However,
as the dimension and the number of local optima of problems
increase, PSO is easily trapped in local optima. We have
proposed an improved PSO algorithm is called EPSO in the
other paper, which has greatly improved the ability of escaping
form local optima. In this paper we applied EPSO to the
structure prediction of toy model both on artificial and real
protein sequences and compared with the results reported in
other literatures. The experimental results demonstrated that
EPSO was efficient in protein structure prediction problem in
toy model.