Computational selection of distinct class- and subclass-specific gene expression signatures
Journal of Biomedical Informatics
Comprehensive vertical sample-based KNN/LSVM classification for gene expression analysis
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Knowledge guided analysis of microarray data
Journal of Biomedical Informatics
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
Constructing the gene regulation-level representation of microarray data for cancer classification
Journal of Biomedical Informatics
A review of feature selection techniques in bioinformatics
Bioinformatics
New gene selection method for multiclass tumor classification by class centroid
Journal of Biomedical Informatics
Journal of Biomedical Informatics
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
A novel feature selection approach for biomedical data classification
Journal of Biomedical Informatics
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
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Gene selection is an important task in bioinformatics studies, because the accuracy of cancer classification generally depends upon the genes that have biological relevance to the classifying problems. In this work, randomization test (RT) is used as a gene selection method for dealing with gene expression data. In the method, a statistic derived from the statistics of the regression coefficients in a series of partial least squares discriminant analysis (PLSDA) models is used to evaluate the significance of the genes. Informative genes are selected for classifying the four gene expression datasets of prostate cancer, lung cancer, leukemia and non-small cell lung cancer (NSCLC) and the rationality of the results is validated by multiple linear regression (MLR) modeling and principal component analysis (PCA). With the selected genes, satisfactory results can be obtained.