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Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation

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...

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Autores principales: Yang, Peng, Li, Xiaoli, Wu, Min, Kwoh, Chee-Keong, Ng, See-Kiong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143124/
https://www.ncbi.nlm.nih.gov/pubmed/21799737
http://dx.doi.org/10.1371/journal.pone.0021502
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author Yang, Peng
Li, Xiaoli
Wu, Min
Kwoh, Chee-Keong
Ng, See-Kiong
author_facet Yang, Peng
Li, Xiaoli
Wu, Min
Kwoh, Chee-Keong
Ng, See-Kiong
author_sort Yang, Peng
collection PubMed
description 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. METHODOLOGY/PRINCIPAL FINDINGS: 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/SIGNIFICANCE: 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.
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spelling pubmed-31431242011-07-28 Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation Yang, Peng Li, Xiaoli Wu, Min Kwoh, Chee-Keong Ng, See-Kiong PLoS One Research Article 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. METHODOLOGY/PRINCIPAL FINDINGS: 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/SIGNIFICANCE: 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. Public Library of Science 2011-07-25 /pmc/articles/PMC3143124/ /pubmed/21799737 http://dx.doi.org/10.1371/journal.pone.0021502 Text en Yang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Yang, Peng
Li, Xiaoli
Wu, Min
Kwoh, Chee-Keong
Ng, See-Kiong
Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
title Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
title_full Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
title_fullStr Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
title_full_unstemmed Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
title_short Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation
title_sort inferring gene-phenotype associations via global protein complex network propagation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3143124/
https://www.ncbi.nlm.nih.gov/pubmed/21799737
http://dx.doi.org/10.1371/journal.pone.0021502
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