A novel hybrid GA/RBFNN technique for protein sequences classification

Xing Ming Zhao; De Shuang Huang; Yiu Ming Cheung

doi:10.2174/0929866053765707

A novel hybrid GA/RBFNN technique for protein sequences classification

Xing Ming Zhao, De Shuang Huang^*, Yiu Ming Cheung

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

9 Citations (Scopus)

Abstract

A novel hybrid genetic algorithm (GA)/radial basis function neural network (RBFNN) technique, which selects features from the protein sequences and trains the RBF neural network simultaneously, is proposed in this paper. Experimental results show that the proposed hybrid GA/RBFNN system outperforms the BLAST and the HMMer.

Original language	English
Pages (from-to)	383-386
Number of pages	4
Journal	Protein and Peptide Letters
Volume	12
Issue number	4
DOIs	https://doi.org/10.2174/0929866053765707
Publication status	Published - May 2005

Scopus Subject Areas

Structural Biology
Biochemistry

User-Defined Keywords

Feature selection
Hybrid GA/RBFNN method
Protein sequences classification

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10.2174/0929866053765707

Cite this

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title = "A novel hybrid GA/RBFNN technique for protein sequences classification",

abstract = "A novel hybrid genetic algorithm (GA)/radial basis function neural network (RBFNN) technique, which selects features from the protein sequences and trains the RBF neural network simultaneously, is proposed in this paper. Experimental results show that the proposed hybrid GA/RBFNN system outperforms the BLAST and the HMMer.",

keywords = "Feature selection, Hybrid GA/RBFNN method, Protein sequences classification",

author = "Zhao, {Xing Ming} and Huang, {De Shuang} and Cheung, {Yiu Ming}",

year = "2005",

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AU - Zhao, Xing Ming

AU - Huang, De Shuang

AU - Cheung, Yiu Ming

PY - 2005/5

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N2 - A novel hybrid genetic algorithm (GA)/radial basis function neural network (RBFNN) technique, which selects features from the protein sequences and trains the RBF neural network simultaneously, is proposed in this paper. Experimental results show that the proposed hybrid GA/RBFNN system outperforms the BLAST and the HMMer.

AB - A novel hybrid genetic algorithm (GA)/radial basis function neural network (RBFNN) technique, which selects features from the protein sequences and trains the RBF neural network simultaneously, is proposed in this paper. Experimental results show that the proposed hybrid GA/RBFNN system outperforms the BLAST and the HMMer.

KW - Feature selection

KW - Hybrid GA/RBFNN method

KW - Protein sequences classification

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U2 - 10.2174/0929866053765707

DO - 10.2174/0929866053765707

M3 - Journal article

C2 - 15907186

AN - SCOPUS:19544395032

SN - 0929-8665

VL - 12

SP - 383

EP - 386

JO - Protein and Peptide Letters

JF - Protein and Peptide Letters

IS - 4

ER -

A novel hybrid GA/RBFNN technique for protein sequences classification

Abstract

Scopus Subject Areas

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