Extracting gene regulation information for cancer classification
Pattern Recognition
The L1-version of the Cramér-von mises test for two-sample comparisons in microarray data analysis
EURASIP Journal on Bioinformatics and Systems Biology
Gene boosting for cancer classification based on gene expression profiles
Pattern Recognition
Correlation-based relevancy and redundancy measures for efficient gene selection
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data
Artificial Intelligence in Medicine
Fundamenta Informaticae - Machine Learning in Bioinformatics
Informative gene selection and tumor classification by null space LDA for microarray data
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
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Motivation: Identification of differentially expressed genes is a major issue in gene expression data analysis and selection of marker genes is critical in tumor classification using gene expression data. In this paper, we propose a semiparametric two-sample test to identify both differentially expressed genes and select marker genes for sample classification. Results: A simulation study shows that the proposed method is more robust and powerful than the methods, generally used such as t-tests and non-parametric rank-sum tests, when the sample size is small. Cross-validation shows that the sample classification based on genes selected using this semiparametric method has lower misclassification rates. Contact: hongyu.zhao@yale.edu