DiME: a scalable disease module identification algorithm with application to glioma progression.

Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the m...

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Autores principales:	Yunpeng Liu, Daniel A Tennant, Zexuan Zhu, John K Heath, Xin Yao, Shan He
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9de7d16156dc44f08df94deea0b8f39f
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spelling	oai:doaj.org-article:9de7d16156dc44f08df94deea0b8f39f2021-11-18T08:33:03ZDiME: a scalable disease module identification algorithm with application to glioma progression.1932-620310.1371/journal.pone.0086693https://doaj.org/article/9de7d16156dc44f08df94deea0b8f39f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24523864/?tool=EBIhttps://doaj.org/toc/1932-6203Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.Yunpeng LiuDaniel A TennantZexuan ZhuJohn K HeathXin YaoShan HePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 2, p e86693 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yunpeng Liu Daniel A Tennant Zexuan Zhu John K Heath Xin Yao Shan He DiME: a scalable disease module identification algorithm with application to glioma progression.
description	Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.
format	article
author	Yunpeng Liu Daniel A Tennant Zexuan Zhu John K Heath Xin Yao Shan He
author_facet	Yunpeng Liu Daniel A Tennant Zexuan Zhu John K Heath Xin Yao Shan He
author_sort	Yunpeng Liu
title	DiME: a scalable disease module identification algorithm with application to glioma progression.
title_short	DiME: a scalable disease module identification algorithm with application to glioma progression.
title_full	DiME: a scalable disease module identification algorithm with application to glioma progression.
title_fullStr	DiME: a scalable disease module identification algorithm with application to glioma progression.
title_full_unstemmed	DiME: a scalable disease module identification algorithm with application to glioma progression.
title_sort	dime: a scalable disease module identification algorithm with application to glioma progression.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/9de7d16156dc44f08df94deea0b8f39f
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DiME: a scalable disease module identification algorithm with application to glioma progression.

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