[en] 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Fonds National de la Recherche - FnR
FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020
[University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science >]
