title: Inferring gene-phenotype associations via global protein complex network propagation.
abstract: BACKGROUND
  Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases.
  
  
  RESULTS
  We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes.
  
  
  CONCLUSIONS
  Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations.
authors.affiliation: Bioinformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore, Singapore.
authors.forename: Peng
authors.initials: P
authors.lastname: Yang
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.forename: Chee-Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: See-Kiong
authors.initials: SK
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D004194:Disease / Q000235:genetics*; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D010641:Phenotype; D060066:Protein Interaction Maps / Q000235:genetics*