Obstructive Nephropathy (ON) is a renal disease and its pathology is believed to be magnified by various molecular processes. In the current study, we apply an intelligent workflow implemented in Rapidminer data mining platform to two different ON datasets. Our scope is to select the most important actors in two corresponding molecular information levels: human miRNA and mouse mRNA. A forward selection method with an embedded nearest neighbor classifier is initially applied to select the most important features in each level. The resulting features are next fed to classifiers appropriately tested utilizing a leave-one-out resampling technique in order to evaluate the relevance of the selected input features when used to classify subjects into output classes defined by ON severity. Preliminary results show that high classification accuracies are obtained, and are supported by the fact that the selected miRNAs or mRNAs have been found significant within differential expression analysis using the same datasets.