Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
Classification consistency analysis for bootstrapping gene selection
Neural Computing and Applications
Artificial Intelligence in Medicine
Direct integration of microarrays for selecting informative genes and phenotype classification
Information Sciences: an International Journal
From protein microarrays to diagnostic antigen discovery
Bioinformatics
Applying genetic algorithms and support vector machines to the gene selection problem
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - VIII Brazilian Symposium on Neural Networks
A hybrid GA/SVM approach for gene selection and classification of microarray data
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Hi-index | 12.05 |
Many subset gene selection methods for microarray data employ classification tools to evaluate the discernability of a gene subset on a specific disease, and this evaluation process generally has a high computational complexity. In this study, we propose a probabilistic mechanism supported by a density-based clustering method and a distance measure to perform individual and group gene replacement for gene selection. Analysts can choose proper values for the parameters of the probabilistic mechanism to set the computational complexity for gene selection. The discernability of a gene subset on classification is evaluated by the distance measure to avoid the language bias that can be introduced by classification tools. Our experimental results on six microarray data sets show that the probabilistic mechanism can effectively and efficiently filter a gene subset with a high discernability on cancer diagnosis.