Algorithms for clustering data
Algorithms for clustering data
The nature of statistical learning theory
The nature of statistical learning theory
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
Principles of data mining
Gene Selection for Multi-Class Prediction of Microarray Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Hybrid Evolutionary Algorithm Based on PSO and GA Mutation
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
A novel hybrid algorithm for function approximation
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Gene extraction for cancer diagnosis by support vector machines-An improvement
Artificial Intelligence in Medicine
Support vector machine for functional data classification
Neurocomputing
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Guest editorial: Computational intelligence and machine learning in bioinformatics
Artificial Intelligence in Medicine
Cancer classification using microarray and layered architecture genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
An ensemble approach applied to classify spam e-mails
Expert Systems with Applications: An International Journal
Ensemble gene selection for cancer classification
Pattern Recognition
Artificial Intelligence in Medicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A two step method to identify clinical outcome relevant genes with microarray data
Journal of Biomedical Informatics
A novel algorithm applied to classify unbalanced data
Applied Soft Computing
An unsupervised approach to feature discretization and selection
Pattern Recognition
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Objective: The type of data in microarray provides unprecedented amount of data. A typical microarray data of ovarian cancer consists of the expressions of tens of thousands of genes on a genomic scale, and there is no systematic procedure to analyze this information instantaneously. To avoid higher computational complexity, it needs to select the most likely differentially expressed gene markers to explain the effects of ovarian cancer. Traditionally, gene markers are selected by ranking genes according to statistics or machine learning algorithms. In this paper, an integrated algorithm is derived for gene selection and classification in microarray data of ovarian cancer. Methods: First, regression analysis is applied to find target genes. Genetic algorithm (GA), particle swarm optimization (PSO), support vector machine (SVM), and analysis of variance (ANOVA) are hybridized to select gene markers from target genes. Finally, the improved fuzzy model is applied to classify cancer tissues. Results: The microarray data of ovarian cancer, obtained from China Medical University Hospital, is used to test the performance of the proposed algorithm. In simulation, 200 target genes are obtained after regression analysis and six gene markers are selected from the hybrid process of GA, PCO, SVM and ANOVA. Additionally, these gene markers are used to classify cancer tissues. Conclusions: The proposed algorithm can be used to analyze gene expressions and has superior performance in microarray data of ovarian cancer, and it can be performed on other studies for cancer diagnosis.