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Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction
Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease ge...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899204/ https://www.ncbi.nlm.nih.gov/pubmed/24465923 http://dx.doi.org/10.1371/journal.pone.0086142 |
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author | Wang, Xiao-Dong Huang, Jia-Liang Yang, Lun Wei, Dong-Qing Qi, Ying-Xin Jiang, Zong-Lai |
author_facet | Wang, Xiao-Dong Huang, Jia-Liang Yang, Lun Wei, Dong-Qing Qi, Ying-Xin Jiang, Zong-Lai |
author_sort | Wang, Xiao-Dong |
collection | PubMed |
description | Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. |
format | Online Article Text |
id | pubmed-3899204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-38992042014-01-24 Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction Wang, Xiao-Dong Huang, Jia-Liang Yang, Lun Wei, Dong-Qing Qi, Ying-Xin Jiang, Zong-Lai PLoS One Research Article Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. Public Library of Science 2014-01-22 /pmc/articles/PMC3899204/ /pubmed/24465923 http://dx.doi.org/10.1371/journal.pone.0086142 Text en © 2014 Wang 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 Wang, Xiao-Dong Huang, Jia-Liang Yang, Lun Wei, Dong-Qing Qi, Ying-Xin Jiang, Zong-Lai Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction |
title | Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction |
title_full | Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction |
title_fullStr | Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction |
title_full_unstemmed | Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction |
title_short | Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction |
title_sort | identification of human disease genes from interactome network using graphlet interaction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899204/ https://www.ncbi.nlm.nih.gov/pubmed/24465923 http://dx.doi.org/10.1371/journal.pone.0086142 |
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