DiME: A Scalable Disease Module Identification Algorithm with Application to Glioma Progression

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.

miRNA-Disease Association Prediction with Collaborative Matrix Factorization

“…Secondly, the known miRNA-disease associations with experimental evidences are still insufficient. The prediction performance of CMFMDA will be improved by integrating more reliable biological information [77][78][79][80][81][82][83][84][85][86]. Finally, how to more reasonably extract and integrate information from biological datasets should be investigated in the future.…”

Cooperative Co-evolutionary Module Identification with Application to Cancer Disease Module Discovery

“…Recently, many MI algorithms have been proposed for disease module identification [56], [57], [58], [59], [60], [61], [62], [63], [64]. According to local hypothesis, all cellular components in the same topological module are very likely to have the same molecular function and thus to be involved in the same disease [55].…”

Integrated regulatory models for inference of subtype‐specific susceptibilities in glioblastoma

“…These models infer genome-wide regulatory links between TFs and target genes primarily using one or more of the following strategies: (i) expression association methods, (ii) analysis of physical binding of TF to promoters and enhancers, and (iii) regression models. Association between the expression of TFs and target genes are quantified by either co-expression metrics (Stuart et al, 2003;Langfelder & Horvath, 2007;Gaiteri et al, 2014;Liu et al, 2014;Aibar et al, 2017) or mutual information (Margolin et al, 2006;Lachmann et al, 2016) to infer potential regulatory relationships. However, these methods do not allow direct inference of causal relationships in transcription regulation, and associationbased links usually need to be filtered based on additional evidence.…”