Artificial neural network model for predicting HIV protease cleavage sites in protein
Advances in Engineering Software
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Machine Learning
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Machine Learning - Special issue on applications in molecular biology
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Classification of heterogeneous gene expression data
ACM SIGKDD Explorations Newsletter
Machine learning for HIV-1 protease cleavage site prediction
Pattern Recognition Letters
Ensemblator: An ensemble of classifiers for reliable classification of biological data
Pattern Recognition Letters
Intelligent Data Analysis - New Methods in Bioinformatics Presented at the Fifth International Conference on Bioinformatics of Genome Regulation and Structure
Information Sciences: an International Journal
Coevolutionary multi-population genetic programming for data classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Attribute selection and rule generation techniques for medical diagnosis systems
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
On the performance of high dimensional data clustering and classification algorithms
Future Generation Computer Systems
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Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. At present, with various learning algorithms available in the literature, researchers are facing difficulties in choosing the best method that can apply to their data. We performed an empirical study on 7 individual learning systems and 9 different combined methods on 4 different biological data sets, and provide some suggested issues to be considered when answering the following questions: (i) How does one choose which algorithm is best suitable for their data set? (ii) Are combined methods better than a single approach? (iii) How does one compare the effectiveness of a particular algorithm to the others?