Predicting disease genes based on normalized protein modules and phenotype ontology

Predicting disease genes in PPI network has attracted a lot of attention over the years. Based on the assumption that the phenotypes of the genes in the same complex where candidate gene located in are more similar to disease, the candidate gene is more possible to be disease gene, we propose a new disease gene identification method based on protein complex phenotype similarity. First, our method mines protein complexes in PPI network by resolution-limit-free clustering algorithm and maps the candidate genes to complexes. Second, we define phenotype similarity according to phenotype ontology, and calculate phenotype similarity value between gene and disease. Third, we add up the phenotype similarity value of whole genes in the complex as weight of candidate gene and rank the candidate gene according to the sum of phenotype similarity value. Finally, we test our method by leave-one-out cross validation. The results show that our method is effective and outperforms other methods such as NetRank, NetScore, NetZcore, Flow, RWR and NDRC. Importantly, we predict the disease gene of Prader-Willi syndrome (MIM: 176270) and Renal tubular dysgenesis (MIM: 267430) successfully, which do not exist in our disease-gene datasets but exists in online databases and scientific publications.