Mining Gene Expression Data using Rough Set Theory

Background: Classification of human tumors into distinguishable entities is traditionally based on clinical, pathohistological, immunohistochemical and cytogenetic data. This classification technique provides classes containing tumors that show similarities but differ strongly in important aspects, e.g. clinical course, treatment response, or survival. New techniques like cDNA microarrays have opened the way to a more accurate stratification of patients with respect to treatment response or survival prognosis, however, reports of correlation between clinical parameters and patient specific gene expression patterns have been extremely rare. One of the reasons is that the adaptation of machine learning approaches to pattern classification, rule induction and detection of internal dependencies within large scale gene expression data is still a formidable challenge for the computer science community. Method: Here we applied a technique based on rough set theory and Boolean reasoning [1,2] implemented into the Rosetta software tool. This technique has already been successfully used to extract descriptive and minimal ‘if-then ’ rules for relating prognostic or diagnostic parameters with particular conditions. The basis of rough set theory is the indiscernibility relation describing the fact that some objects of the universe are not discerned in view of the information forming