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

Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S - PLoS ONE (2014)

Bottom Line: 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.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.Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, University of Birmingham, Birmingham, United Kingdom.

ABSTRACT
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.

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General work flow for the DiME framework.
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pone-0086693-g001: General work flow for the DiME framework.

Mentions: A general work flow of the DiME framework for disease module identification and analysis is given in Figure 1. Note that our framework is readily adaptable to other types of study. For example, the construction of co-expression networks may be replaced by PPI networks to examine protein complexes or signaling modules, and the procedures downstream of the statistical significance evaluation step may also be varied according to specific aims of research, e.g. validation of disease modules via prediction of patient recovery/survival instead of correlating with tumour grade in our case study. In the following sub-sections, we provide details for the core steps of the DiME work flow - network construction, module extraction algorithm and evaluation of statistical significance.


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

Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S - PLoS ONE (2014)

General work flow for the DiME framework.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921127&req=5

pone-0086693-g001: General work flow for the DiME framework.
Mentions: A general work flow of the DiME framework for disease module identification and analysis is given in Figure 1. Note that our framework is readily adaptable to other types of study. For example, the construction of co-expression networks may be replaced by PPI networks to examine protein complexes or signaling modules, and the procedures downstream of the statistical significance evaluation step may also be varied according to specific aims of research, e.g. validation of disease modules via prediction of patient recovery/survival instead of correlating with tumour grade in our case study. In the following sub-sections, we provide details for the core steps of the DiME work flow - network construction, module extraction algorithm and evaluation of statistical significance.

Bottom Line: 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.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.Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, University of Birmingham, Birmingham, United Kingdom.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus