PROTEIN SECONDARY STRUCTURE PREDICTION USING KNOWLEDGE-BASED POTENTIALS

被引：0

作者：

Saraswathi, Saras ^{[1
]}

Jernigan, Robert L. ^{[1
]}

Kloczkowski, Andrzej ^{[1
]}

Kolinski, Andrzej ^{[2
]}

机构：

[1] LH Baker Ctr Bioinformat & Biol Stat, Dept Biochem Biophys & Mol Biol, 112 Off & Lab Bldg, Ames, IA 50011 USA

[2] Warsaw Univ, Lab Theory Biopolymers, Fac Chem, PL-02093 Warsaw, Poland

来源：

ICFC 2010/ ICNC 2010: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FUZZY COMPUTATION AND INTERNATIONAL CONFERENCE ON NEURAL COMPUTATION | 2010年

基金：

美国国家卫生研究院;

关键词：

Protein secondary structure prediction; Neural networks; Extreme learning machine; Particle swarm optimization; SUPPORT VECTOR MACHINES; EVOLUTIONARY INFORMATION; ACCURACY; SEQUENCE; ALIGNMENT; SERVER;

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：

A novel method is proposed for predicting protein secondary structure using data derived from knowledge based potentials and Neural Networks. Potential energies for amino acid sequences in proteins are calculated using protein structures. An Extreme Learning Machine classifier (ELM-PSO) is used to model and predict protein secondary structures. Classifier performance is maximized using the Particle Swarm Optimization algorithm. Preliminary results show improved results.

引用

页码：370 / 375

页数：6

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