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A Novel Hybrid GA/RBFNN Technique for Protein Sequences Classification

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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

Document Type: Review Article

DOI: http://dx.doi.org/10.2174/0929866053765707

Affiliations: Intelligent Computing Lab, Hefei Institute of Intelligent Machines, CAS, Hefei, Anhui, 230031, China.

Publication date: May 1, 2005

More about this publication?
  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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