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


METHODS
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.


CONCLUSIONS
This predictive likelihood-based classifier performs well and is simple to understand. Empirical examination revealed good classification accuracy using relatively few genes.
Yang
Tae Young
TY
Department of Mathematics, Myongji University, Yongin, Kyonggi, Republic of Korea. tyang@mju.ac.kr
D019313:BRCA1 Protein / Q000235:genetics; D024682:BRCA2 Protein / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D003951:Diagnostic Errors; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm; D006801:Humans; D007938:Leukemia / Q000235:genetics; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*; D011237:Predictive Value of Tests; D015203:Reproducibility of Results
D019313:BRCA1 Protein; D024682:BRCA2 Protein
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