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A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network
The interactions among the genes within a disease are helpful for better understanding the hierarchical structure of the complex biological system of it. Most of the current methodologies need the information of known interactions between genes or proteins to create the network connections. However,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4923884/ https://www.ncbi.nlm.nih.gov/pubmed/27349736 http://dx.doi.org/10.1038/srep28720 |
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author | Xu, Junmei Jing, Runyu Liu, Yuan Dong, Yongcheng Wen, Zhining Li, Menglong |
author_facet | Xu, Junmei Jing, Runyu Liu, Yuan Dong, Yongcheng Wen, Zhining Li, Menglong |
author_sort | Xu, Junmei |
collection | PubMed |
description | The interactions among the genes within a disease are helpful for better understanding the hierarchical structure of the complex biological system of it. Most of the current methodologies need the information of known interactions between genes or proteins to create the network connections. However, these methods meet the limitations in clinical cancer researches because different cancers not only share the common interactions among the genes but also own their specific interactions distinguished from each other. Moreover, it is still difficult to decide the boundaries of the sub-networks. Therefore, we proposed a strategy to construct a gene network by using the sparse inverse covariance matrix of gene expression data, and divide it into a series of functional modules by an adaptive partition algorithm. The strategy was validated by using the microarray data of three cancers and the RNA-sequencing data of glioblastoma. The different modules in the network exhibited specific functions in cancers progression. Moreover, based on the gene expression profiles in the modules, the risk of death was well predicted in the clustering analysis and the binary classification, indicating that our strategy can be benefit for investigating the cancer mechanisms and promoting the clinical applications of network-based methodologies in cancer researches. |
format | Online Article Text |
id | pubmed-4923884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-49238842016-06-28 A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network Xu, Junmei Jing, Runyu Liu, Yuan Dong, Yongcheng Wen, Zhining Li, Menglong Sci Rep Article The interactions among the genes within a disease are helpful for better understanding the hierarchical structure of the complex biological system of it. Most of the current methodologies need the information of known interactions between genes or proteins to create the network connections. However, these methods meet the limitations in clinical cancer researches because different cancers not only share the common interactions among the genes but also own their specific interactions distinguished from each other. Moreover, it is still difficult to decide the boundaries of the sub-networks. Therefore, we proposed a strategy to construct a gene network by using the sparse inverse covariance matrix of gene expression data, and divide it into a series of functional modules by an adaptive partition algorithm. The strategy was validated by using the microarray data of three cancers and the RNA-sequencing data of glioblastoma. The different modules in the network exhibited specific functions in cancers progression. Moreover, based on the gene expression profiles in the modules, the risk of death was well predicted in the clustering analysis and the binary classification, indicating that our strategy can be benefit for investigating the cancer mechanisms and promoting the clinical applications of network-based methodologies in cancer researches. Nature Publishing Group 2016-06-28 /pmc/articles/PMC4923884/ /pubmed/27349736 http://dx.doi.org/10.1038/srep28720 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Xu, Junmei Jing, Runyu Liu, Yuan Dong, Yongcheng Wen, Zhining Li, Menglong A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
title | A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
title_full | A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
title_fullStr | A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
title_full_unstemmed | A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
title_short | A new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
title_sort | new strategy for exploring the hierarchical structure of cancers by adaptively partitioning functional modules from gene expression network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4923884/ https://www.ncbi.nlm.nih.gov/pubmed/27349736 http://dx.doi.org/10.1038/srep28720 |
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