Bioinformatics Advance Access originally published online on January 25, 2005
Bioinformatics 2005 21(9):1831-1837; doi:10.1093/bioinformatics/bti281
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks
Department of Computer Science, Exeter University Exeter, Devonshire, UK
Motivation: Apoptosis has drawn the attention of researchers because of its importance in treating some diseases through finding a proper way to block or slow down the apoptosis process. Having understood that caspase cleavage is the key to apoptosis, we find novel methods or algorithms are essential for studying the specificity of caspase cleavage activity and this helps the effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, there is a motivation, in this study, to investigate the application of bio-basis function neural networks for the prediction of caspase cleavage sites.
Results: Thirteen protein sequences with experimentally determined caspase cleavage sites were downloaded from NCBI. Bayesian bio-basis function neural networks are investigated and the comparisons with single-layer perceptrons, multilayer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size used to generate sub-sequences for modelling on prediction accuracy is studied. The results show that the Bayesian bio-basis function neural network with two Gaussian distributions for model parameters (weights) performed the best and the highest prediction accuracy is 97.15 ± 1.13%.
Availability: The package of Bayesian bio-basis function neural network can be obtained by request to the author.
Contact: z.r.yang{at}ex.ac.uk
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  L. J.K. Wee, T. W. Tan, and S. Ranganathan CASVM: web server for SVM-based prediction of caspase substrates cleavage sites Bioinformatics, December 1, 2007; 23(23): 3241 - 3243. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. R. Yang Mining SARS-CoV protease cleavage data using non-orthogonal decision trees: a novel method for decisive template selection Bioinformatics, June 1, 2005; 21(11): 2644 - 2650. [Abstract] [Full Text] [PDF]
|
|