Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Artificial Intelligence in Medicine
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
A review of feature selection techniques in bioinformatics
Bioinformatics
Monte Carlo feature selection for supervised classification
Bioinformatics
Locally Linear Discriminant Embedding for Tumor Classification
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Computational Statistics & Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bayesian binary kernel probit model for microarray based cancer classification and gene selection
Computational Statistics & Data Analysis
A parallel classification and feature reduction method for biomedical applications
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Meta analysis algorithms for microarray gene expression data using Gene Regulatory Networks
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Improving accuracy of microarray classification by a simple multi-task feature selection filter
International Journal of Data Mining and Bioinformatics
Gene feature extraction using T-test statistics and kernel partial least squares
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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Identifying glioma cancer-alerted genetic markers through analysis of microarray data allows us to detect tumours at the genome-wide level. To this end, we propose to identify glioma gene markers based primarily on their correlation with the glioma diagnostic outcomes, rather than merely on the classification quality or differential expression levels, as it is not the classification or expression level per se that is crucial, but the selection of biologically relevant biomarkers is the most important issue. With the help of singular value decomposition, microarray data are decomposed and the eigenvectors corresponding to the biological effect of diagnostic outcomes are identified. Genes that play important roles in determining this biological effect are thus detected. Therefore, genes are essentially identified in terms of their strength of association with diagnostic outcomes. Monte Carlo simulations are then used to fine tune the selected gene set in terms of classification accuracy. Experiments show that the proposed method achieves better classification accuracies and is data sets independent. Graph-based statistical analysis showed that the selected genes have close relationships with glioma diagnostic outcomes. Further biological database and literature study confirms that the identified genes are biologically relevant.