Skip Navigation



Bioinformatics Advance Access published online on January 25, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti281
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/9/1831    most recent
bti281v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Yang, Z. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yang, Z. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics © Oxford University Press 2005; all rights reserved.
Received May 11, 2004
Revised December 20, 2004
Accepted January 18, 2005

Article

Prediction of caspase cleavage sites using Bayesian bio-basis function neural networks

Zheng Rong Yang 1

1 Department of Computer Science, Exeter University, UK


  Abstract

Motivation: Apoptosis has drawn the attention for research 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, novel methods or algorithms are then essential for studying the specificity of caspase cleavage activity, hence helping effective drug design. As bio-basis function neural networks have proven to outperform some conventional neural learning algorithms, it is motivated in this study to investigate the application of bio-basis function neural networks to the prediction of caspase cleavage sites.

Result: 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, multi-layer perceptrons, the original bio-basis function neural networks and support vector machines are given. The impact of the sliding window size which is used to generate sub-sequences for modelling on prediction accuracy is studied. The result shows 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.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
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]

Home page
 BioinformaticsHome page
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]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.