A Novel Algorithm for Prioritizing Disease Candidate Genes from the Weighted PPI Network Computational methods accurately prioritizing latent disorder genes require for all kinds of biological information. But for the defection of a single type of biological data has a negative impact on the identification of genes causing diseases. To address the limitation, computing approaches often integrate different type of biological data. In the study, a novel algorithm PDGPC (Predicting Disease Genes with Protein Complexes) is proposed. It utilizes protein subcellular localizations to improve the reliability of the proteinprotein interactions and constructs the weighted networks. And then, PDGPC builds the diseasespecific networks by utilizing the protein complexes which are detected from the weighted networks through the nonnegative matrix factorization. Finally, PDGPC scores all proteins in the diseasespecific networks in terms of WDC. The literature retrieving method tests the correlations of top genes with more higher scores with diseases. Results show PDGPC discover some novel candidate disease genes which are valuable references for the biomedical scientists.

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.