The effect of the different training samples is
different for the classifier when pattern recognition system is established.
The training samples were selected randomly in the past protein disulfide bond
prediction methods, therefore the prediction accuracy of protein contact was
reduced. In order to improve the influence of training samples, a prediction method
of protein disulfide bond on the basis of pattern selection and Radical Basis
Function neural network has been brought forward in this paper. The attributes
related with protein disulfide bond are extracted and coded in the method and
pattern selection is used to select training samples from coded samples in
order to improve the precision of protein disulfide bond prediction. 200
proteins with disulfide bond structure from the PDB database are encoded
according to the encoding approach and are taken as models of training samples.
Then samples are taken on the pattern selection based on the nearest neighbor
algorithm and corresponding prediction models are set by using RBF neural
network. The simulation experiment result indicates that this method of pattern
selection can improve the prediction accuracy of protein disulfide bond.