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.
Support Vector Machine Acute Lymphocytic Leukemia Cancer Type Acute Myelogenous Leukemia Expression Measurement
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I am very grateful to the editor and anonymous referees for their constructive comments on a draft of this paper.
This work was supported by a Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) [grant no: KRF-2006-312-C00493].
The author has no conflicts of interest that are directly relevant to the content of this study.
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