Making large-scale support vector machine learning practical
Advances in kernel methods
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Analysis of gene expression profiles: class discovery and leaf ordering
Proceedings of the sixth annual international conference on Computational biology
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Classifiers, as one of the important tools of analyzing gene expression data in the post-genomic epoch, have been used widely in the classification of different cancer types in the past few years. Although most existing classifiers have high classification accuracy, the process of classification is a black box and they can not give biologists more information and interpretable results of classification. In this paper, we propose a novel visualization cancer classification method. Besides offering high classification accuracy, the method can help us identify complex disease-related genes and assess gene expression variation during the process of classification. The results of classification are natural and interpretable and the process of classification is visible. To evaluate the performance of the method we have applied the proposed method to three public data sets. The experimental results demonstrate that the approach is feasible and useful.