Advances in neural information processing systems 2
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An accelerated procedure for recursive feature ranking on microarray data
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Variable selection using svm based criteria
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Identifying radiation exposure biomarkers from mouse blood transcriptome
International Journal of Bioinformatics Research and Applications
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Microarrays serve scientists as a powerful and efficient tool to observe thousands of genes and analyse their activeness in normal or cancerous tissues. In general, microarrays are used to measure the expression levels of thounsands of genes in a cell mixture. Gene expression data obtained from microarrays can be used for various applications. One such application is that of gene selection. Gene selection is very similar to the feature selection problem addressed in the machine-learning area. In a nutshell, gene selection is the problem of identifying a minimum set of genes that are responsible for certain events (for example the presence of cancer). Informative gene selection is an important problem arising in the analysis of microarray data. In this paper, we present a novel algorithm for gene selection that combines Support Vector Machines (SVMs) with gene correlations. Experiments show that the new algorithm, called GCI-SVM, obtains a higher classification accuracy using a smaller number of selected genes than the well-known algorithms in the literature.