Classification of microarrays to nearest centroids
Bioinformatics
The-more-the-better and the-less-the-better
Bioinformatics
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Journal of Biomedical Informatics
Multiclass Gene Selection Using Pareto-Fronts
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Journal of Biomedical Informatics
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In the analysis of gene expression profiles, the selection of genetic markers and precise diagnosis of cancer type are crucial for successful treatment. The selection of discriminatory genes is critical to improve the accuracy and decrease computational complexity and cost in microarray analysis. In this paper, we developed a new statistical parameter, the suitability score to filter genes which only utilize sample distances from the class centroid. The filtered genes are employed in the nearest centroid classification to classify cancer. To evaluate the performance of the new statistical parameter, the proposed approach is applied to three publicly available microarray datasets. In this paper we demonstrate that the proposed gene selection method is steady in handling classification tasks and is a useful tool for gene selection and mining high dimension data.