Network perturbation analysis of omics data for complex diseases using convex optimization

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network tend to undergo coordinated expression changes. We describe an approach that allows identifying such jointly differentially expressed genes from input expression data and a graph encoding pairwise functional associations between genes (such as protein interactions). We cast this as a feature selection problem in penalized two-class (cases vs. controls) classification, and we propose a novel pairwise elastic net (PEN) penalty that favors the selection of discriminative genes according to their connectedness in the interaction graph. Experiments on large-scale gene expression data for Parkinson’s disease demonstrate marked improvements in feature grouping over competitive methods.