Graph-constrained discriminant analysis of functional genomics data

Classification studies from microarray data have proved useful in tasks like predicting patient class. At the same time, more and more biological information about gene regulation networks has been gathered mainly in the form of graph. Incorporating the a priori biological information encoded by graphs turns out to be a very important issue to increase classification performance. We present a method to integrate information from a network topology into a classification algorithm: the graph-Constrained Discriminant Analysis (gCDA). We applied our algorithm to simulated and real data and show that it performs better than a linear Support Vector Machines classifier.