A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Determining the Significance of Input Parameters using Sensitivity Analysis
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
A tutorial on support vector regression
Statistics and Computing
Computer science and bioinformatics
Communications of the ACM - The disappearing computer
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feed-forward artificial neural network based inference system applied in bioinfonnatics data-mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Dynamic penalty based GA for inducing fuzzy inference systems
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Bioinformatics integration framework for metabolic pathway data-mining
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Bioinformatics with soft computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper describes the development of an inference system used for the identification of genes that encode enzymes of metabolic pathways. Input sequence alignment values are used to classify the best candidate genes for inclusion in a metabolic pathway map. The system workflow allows the user to provide feedback, which is stored in conjunction with analysed sequences for periodic retraining. The construction of the system involved the study of several different classifiers with various topologies, data sets and parameter normalisation data models. Experimental results show an excellent prediction capability with the classifiers trained with mixed data providing the best results.