Molecular diagnosis of tumor based on independent component analysis and support vector machines

Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient tumor diagnosis. For more accurate classification of tumor, extracting discriminant components from thousands of genes is an important problem which becomes a challenging task due to its characteristics such as the large number of genes and small sample size. We propose a novel approach which combines gene ranking with independent component analysis that has been developing recently to further improve the classification performance of gene expression data based on support vector machines. Two sets of gene expression data (colon dataset and leukemia dataset) are examined to confirm that the proposed approach can extract a small quantity of independent components which can drastically reduce the dimensionality of the original gene expression data when retaining higher recognition rate. The cross-validation accuracy of 100% has been achieved with extracting only 3 independent components from the leukemia dataset, and 93.55% for the colon dataset.