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With the increasing expectation on personalized medicine, it is getting importance to analyze medical information in molecular biology perspective. Gene expression data are one of representative ones to show the microscopic phenomena of biological activities. In gene expression data analysis, one of major concerns is to identify markers which can be used to predict disease occurrence, progression or recurrence in the molecular level. Existing markers candidate identification methods mainly depend on statistical hypothesis test methods. This paper proposes a search method based decision tree induction to identify candidate markers which consist of multiple genes. The propose method discretizes numeric expression level into three categorical values and allows candidate markers` genes to be expressed by their negation as well as categorical values. It is desirable to have some number of genes to be included in markers. Hence the method is devised to try to find candidate markers with restricted number of genes.

Candidate Marker Identification from Gene Expression Data with Attribute Value Discretization and Negation