The simple classification of multiple cancer types using a small number of significant genes.

BACKGROUND AND
OBJECTIVE: The problems involved in the classification of cancers have recently received a great deal of attention in the context of DNA microarrays. We propose a simple procedure for classifying or predicting the cancer types of test samples when multiple cancer types and many genes are present.
METHOD: The procedure sequentially combines a gene-sort algorithm and a predictive likelihood-based classifier. Genes that have homogeneous patterns of expression measurements across cancer types are of limited interest. Therefore, this algorithm orders genes on the basis of strong heterogeneous patterns. The proposed classifier then selects the first few genes, which are sufficient to classify most training samples correctly via cross validation. Test samples were classified using only the selected genes. RESULTS AND
CONCLUSION: This predictive likelihood-based classifier performs well and is simple to understand. Empirical examination revealed good classification accuracy using relatively few genes.