The study proposes a decision tree based classification of gene expression and protein display data. The problem tackled in the research is accurate classification of records that hold gene or protein microarray data of cancer patients and healthy donors. The data available to discriminate between conditions is overwhelming for most classical classifiers because of the large dimensionality – while the number of patients is scarce due to the high costs of the tests, the number of genes or proteins tested simultaneously is very large (several thousand). The study proposes using random subspace method with genetic algorithms to generate decision tree classifiers for bioinformatics microarray data.