Before revision:  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 .
After revision:  So, here, we propose computational advances towards improving prediction accuracy from a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process. Here, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related gene candidates for future wet lab validation .