Discriminating graph pattern mining from gene expression data

Here we consider the problem of mining gene expression data in order to single out interesting features characterizing healthy/ unhealthy samples of an input dataset. The presented approach is based on a network model of the input gene expression data, where there is a labeled graph for each sample. This is the first attempt to build a different graph for each sample and, then, to have a database of graphs for representing a sample set. The main goal is that of singling out interesting differences between healthy and unhealthy samples, through the extraction of discriminative patterns among graphs belonging to the two different sample sets. Differently from the other approaches presented in the literature, this technique is able to take into account important local similarities, and also collaborative effects involving interactions between multiple genes. In particular, edge-labeled graphs are employed and the discriminative power of a pattern is measured on the basis of edge weights, which are representative of how much relevant is the co-expression between two genes.