![]() ), a major drawback of the community detection algorithm is the resolution limit problem which results in huge modules with large numbers of genes (e.g., in ). Though it is capable of yielding biological insight in several case studies (e.g. One of the most popular algorithms is community detection algorithm that maximises a modularity measure brought forth by Newman (2006). Several module identification algorithms was previously applied. To identify disease modules with high confidence, the first and most important step is the identification of significant and robust topological modules in a network constructed from patient data (e.g., gene co-expression network built from tumour microarray data). A primary objective in network medicine, therefore, is to integrate the topological modules of biological networks and functional annotation to identify disease modules that contain both known and unknown disease genes and potential therapeutic targets. It has been argued that there is a significant overlap among the topological module (e.g., highly interlinked local region in the network), the functional module (e.g., a group of molecular components responsible for a particular cellular process), and the disease module consisting of disease-associated genes. ![]() The fundamental hypothesis is that the impact of genetic and environmental disturbance upon disease phenotype is likely to be asserted through coordinated activity of a group of genes and their products which interact intensively, termed as disease modules. ![]() The implications of methodology in network biology have been taken a step further by network medicine which focuses on the application to the understanding of complex disease pathophysiology. Viewing the functional machinery of the cell as a complex network of physical and logical interactions rather than a simple assembly of individual functional components has contributed unprecedented insight into the cell's wiring scheme. With the increasing availability of high-throughput, genome-wide assay data and high-performance computational resources, network biology (systematically reviewed by Barabási in ), which addresses the intrinsic structure and organisation of networks of pairwise biological interactions, has rapidly evolved as a promising research area. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. 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. 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. 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. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. ![]() 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. This paper proposes a novel algorithm, DiME ( Disease Module Extraction), to identify putative disease modules from biological networks. 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. Disease module is a group of molecular components that interact intensively in the disease specific biological network. ![]()
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