Use of Evolutionary Hypernetworks for Mining Prostate Cancer Data
Hypernetwork models have been proposed as a random graph model of learning and memory inspired by biomolecular networks in the cell. The hypergraph structure of the hypernetworks turned out to be very useful for discovering the building blocks of higher order interaction of multiple variables. Here we use the hypernetwork model for the analysis of microarray data for cancer diagnosis. Tested on a prostate cancer problem consisting of 102 training examples and 34 test examples of 225 features, the hypernetwork models outperformed the accuracy of multilayer perceptrons and decision trees. We also analyze the modules discovered by the hypernetwork models as potential macro biomarkers for molecular diagnosis.