Graph-constrained discriminant analysis of functional genomics data

Abstract

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 graphConstrained 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.

Gene ranking and biomarker discovery under correlation

“…Finally, additional information about the structure of the correlation matrix P (or its inverse) may also be taken into account when estimating the cat score. This is done simply by replacing the unrestricted shrinkage estimator by a more structured estimator (e.g., Tai and Pan, 2007;Li and Li, 2008;Guillemot et al, 2008).…”

Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process

“…Here, we present a fully operational and validated method that has resulted from preliminary works reported in [8]. In the discriminant analysis (DA), the decision function involves the inverse of the within-class covariance matrix.…”

“…In this paper, we propose a new method - called graph constrained discriminant analysis (gCDA) - , which is a constrained version of the discriminant analysis [7] , with constraint depending on information that is represented by one or more graphs. Here, we present a fully operational and validated method that has resulted from preliminary works reported in [8] . In the discriminant analysis (DA), the decision function involves the inverse of the within-class covariance matrix.…”