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1 Journal of Medical Informatics & Technologies

1 2000

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Cancer classification based on gene expression data

Fujarewicz K., Rzeszowska-Wolny J.

Journal of Medical Informatics & Technologies

2000

Vol. 1

BI 23--27

Proper classification of cancer is a crucial aspect in diagnosis and choosing optimal medical therapy. It has been suggested, in recent years, that classification process of cancer can be done using gene expression monitoring. Usefulness of this approach has increased due to the new technique of gene expression monitoring – using so called "expression chips". Recently in [1, 3] a heuristic method of cancer classification, called weighted voting (WV) method, based on gene expression levels has been proposed and tested on a set of samples of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Here a more traditional approach to feature selection and classification is presented and tested on the same data set. Feature selection is performed using modified Sebestyen criterion and classification is done using linear classifying function trained by modified perception algorithm. Obtained results are better than results of the WV method. In cross-validation of initial set all 38 samples were classified correctly (WV – 1 incorrect) and only one sample from independent set was classified incorrectly (WV – 2 incorrect).