C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Neural networks for molecular sequence classification
Mathematics and Computers in Simulation
Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Towards a comprehensive collection of diagnostic patterns for protein sequence classification
Information Sciences—Informatics and Computer Science: An International Journal
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Fast learning in networks of locally-tuned processing units
Neural Computation
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
IEEE Transactions on Knowledge and Data Engineering
Protein sequence analysis using relational soft clustering algorithms
International Journal of Computer Mathematics - Bioinformatics
Predicting palmitoylation sites using a regularised bio-basis function neural network
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Bio-kernel self-organizing map for HIV drug resistance classification
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Classifying G-protein coupled receptors with bagging classification tree
Computational Biology and Chemistry
Relevant and Non-Redundant Amino Acid Sequence Selection for Protein Functional Site Identification
International Journal of Software Science and Computational Intelligence
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Protein phosphorylation is a post-translational modification performed by a group of enzymes known as the protein kinases or phosphotransferases (Enzyme Commission classification 2.7). It is essential to the correct functioning of both proteins and cells, being involved with enzyme control, cell signalling and apoptosis. The major problem when attempting prediction of these sites is the broad substrate specificity of the enzymes. This study employs back-propagation neural networks (BPNNs), the decision tree algorithm C4.5 and the reduced bio-basis function neural network (rBBFNN) to predict phosphorylation sites. The aim is to compare prediction efficiency of the three algorithms for this problem, and examine knowledge extraction capability. All three algorithms are effective for phosphorylation site prediction. Results indicate that rBBFNN is the fastest and most sensitive of the algorithms. BPNN has the highest area under the ROC curve and is therefore the most robust, and C4.5 has the highest prediction accuracy. C4.5 also reveals the amino acid 2 residues upstream from the phosporylation site is important for serine/threonine phosphorylation, whilst the amino acid 3 residues upstream is important for tyrosine phosphorylation.