Supervised prediction of agingrelated genes from a contextspecific protein interaction subnetwork Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human agingrelated genes. We focus on supervised prediction of such genes. Gene expressionbased methods for this purpose study genes in isolation from each other. While proteinprotein interaction (PPI) networkbased methods for this purpose account for interactions between genesu0027 protein products, current PPI network data are contextunspecific, spanning different biological conditions. Instead, here, we focus on an agingspecific subnetwork of the entire PPI network, obtained by integrating agingspecific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting agingrelated genes from an agingspecific PPI subnetwork. We find that using an agingspecific subnetwork indeed yields more accurate agingrelated gene predictions than using the entire network. Also, predictive methods from our framework that have not previously been used for supervised prediction of agingrelated genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.

Factors Affecting NetworkBased Gene Prediction Across Diverse Diseases There are many current efforts to integrate biological interaction data with disease information in order to predict new genes associated with complex diseases. Networkbased learning methods such as logistic regression can utilize this information to identify disease genes, and are typically applied to proteinprotein interaction networks. However, little is reported about what factors influence the performance of these networkbased methods. Here, we explore features that affect networkbased disease gene prediction performance. We devise two crossvalidation schemes to evaluate the impact of various parameters, settings and disease qualities across a wide range of diseases. We demonstrate that including gene regulatory interactions and including lowconfidence disease genes improves disease gene prediction performance. Further, network connectivity among highconfidence disease genes is a strong indicator of prediction performance. We demonstrate that network and input features can have a dramatic effect on prediction performance, and these should be carefully considered when designing networkbased algorithms to find new disease genes.