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Background. Human aging is linked to many prevalent diseases. The agingprocess is highly influenced by genetic factors. Hence, it is important toidentify human aging-related genes. We focus on supervised prediction of suchgenes. Gene expression-based methods for this purpose study genes in isolationfrom each other. While protein-protein interaction (PPI) network-based methodsfor this purpose account for interactions between genes' protein products,current PPI network data are context-unspecific, spanning different biologicalconditions. Instead, here, we focus on an aging-specific subnetwork of theentire PPI network, obtained by integrating aging-specific gene expression dataand PPI network data. The potential of such data integration has beenrecognized but mostly in the context of cancer. So, we are the first to proposea supervised learning framework for predicting aging-related genes from anaging-specific PPI subnetwork.  Results. In a systematic and comprehensive evaluation, we find that in manyof the evaluation tests: (i) using an aging-specific subnetwork indeed yieldsmore accurate aging-related gene predictions than using the entire network, and(ii) predictive methods from our framework that have not previously been usedfor supervised prediction of aging-related genes outperform existing prominentmethods for the same purpose.  Conclusion. These results justify the need for our framework.

Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork