Iteration method for detecting disease genes in terms of the integration of the cellular compartment information with the protein-protein interaction data

Abstract

Many computational approaches identify disease genes based on the protein-protein interaction (PPI) networks because of the principle 'Guilt-by-Associate'. However, the defects of the PPI data severely reduce the accuracy of the predicting methods. In the current study, a new framework called IMIDG is developed to identify causal genes for diseases. First, the reliability of the interactions among proteins is quantified by incorporating the subcellular localisation information into the human PPI networks and the weighted networks are built. Based on the weighted PPI networks, an iteration function is performed to score and rank the disease candidate genes. The leave-one-out crossing validation (LOOCV) and literature study method are used to test IMIDG, DADA and ToppNet algorithms. The areas under curves show that IMIDG outperforms DADA and ToppNet methods in prioritising disease candidate genes. Additionally, out of the 18 novel genes in the top 50 gene set, five genes are proved to be associated with colorectal cancer by the literatures, suggesting the remaining genes for further investigation.