Skip to main content

A Novel Hybrid GA/RBFNN Technique for Protein Sequences Classification

Buy Article:

$55.00 plus tax (Refund Policy)

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.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: feature selection; hybrid ga/rbfnn method; protein sequences classification

Document Type: Review Article

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

Publication date: 2005-05-01

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.
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more