Motivation: This study focuses on supervised prediction of aging-related genes from -omics data. Unlike gene expression methods that capture aging-specific information but study genes in isolation, or protein-protein interaction (PPI) network methods that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. Results: So, here, we propose computational advances towards improving prediction accuracy from a dynamic aging-specific subnetwork. We develop a supervised learning model that when applied to a dynamic subnetwork yields extremely high prediction performance, with F-score of 91.4\%, while the best model on any static subnetwork yields F-score of "only" 74.3\%. Hence, our predictive model could guide with high confidence the discovery of novel aging-related gene candidates for future wet lab validation. Contact: tmilenko@nd.edu
 This study focuses on supervised prediction of aging-related genes from -omics data. Unlike gene expression methods that capture aging-specific information but study genes in isolation, or protein-protein interaction (PPI) network methods that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. So, here, we propose computational advances towards improving prediction accuracy from a dynamic aging-specific subnetwork. We develop a supervised learning model that when applied to a dynamic subnetwork yields extremely high prediction performance, with F-score of 91.4\%, while the best model on any static subnetwork yields F-score of "only" 74.3\%. Hence, our predictive model could guide with high confidence the discovery of novel aging-related gene candidates for future wet lab validation. 
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Motivation:
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Contact: tmilenko@nd.edu