Prioritization of candidate genes based on disease similarity and protein´s proximity in PPI networks

Identifying the genes causing a genetic disease is a key challenge in human health. Recently molecular interaction data has been used to prioritize candidate genes with respect to a particular disease. As a result, different methods have been implemented to rank genes which cause a given disease. However it has been suggested in literature that, to prioritize candidate genes it is necessary to consider disease similarity along with the protein´s proximity to disease genes in a protein-protein interaction (PPI) network. This paper proposes a new algorithm called proximity disease similarity algorithm (ProSim) which considers both properties simultaneously. Prostate cancer, Alzheimer disease and diabetes mellitus type 2 case studies are then used to test the proposed method. Results in terms of leave-one-out cross validation and ROC curves indicate that the proposed approach outperforms existing methods.