Identifying communities from multiplex biological networks by randomized optimization of modularity

2018 ◽

Vol 7 ◽

pp. 1042 ◽

Cited By ~ 1

Author(s):

Gilles Didier ◽

Alberto Valdeolivas ◽

Anaïs Baudot

Keyword(s):

Biological Networks ◽

Disease Genes ◽

Module Identification ◽

Gwas Dataset ◽

Disease Module ◽

Common Operation ◽

Network Modularity ◽

Disease Community

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challenge benchmark, the results strongly depend on the selected GWAS dataset and enrichment p-value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.

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An unsupervised disease module identification technique in biological networks using novel quality metric based on connectivity, conductance and modularity

F1000Research ◽

10.12688/f1000research.14258.1 ◽

2018 ◽

Vol 7 ◽

pp. 378 ◽

Cited By ~ 5

Author(s):

Raghvendra Mall ◽

Ehsan Ullah ◽

Khalid Kunji ◽

Michele Ceccarelli ◽

Halima Bensmail

Keyword(s):

Biological Networks ◽

Complex Traits ◽

Genome Wide Association Study ◽

Module Identification ◽

Quality Metric ◽

Genome Wide ◽

Refinement Method ◽

Disease Module ◽

Identification Technique ◽

Evaluation Metric

Disease processes are usually driven by several genes interacting in molecular modules or pathways leading to the disease. The identification of such modules in gene or protein networks is the core of computational methods in biomedical research. With this pretext, the Disease Module Identification (DMI) DREAM Challenge was initiated as an effort to systematically assess module identification methods on a panel of 6 diverse genomic networks. In this paper, we propose a generic refinement method based on ideas of merging and splitting the hierarchical tree obtained from any community detection technique for constrained DMI in biological networks. The only constraint was that size of community is in the range [3, 100]. We propose a novel model evaluation metric, called F-score, computed from several unsupervised quality metrics like modularity, conductance and connectivity to determine the quality of a graph partition at given level of hierarchy. We also propose a quality measure, namely Inverse Confidence, which ranks and prune insignificant modules to obtain a curated list of candidate disease modules (DM) for biological network. The predicted modules are evaluated on the basis of the total number of unique candidate modules that are associated with complex traits and diseases from over 200 genome-wide association study (GWAS) datasets. During the competition, we identified 42 modules, ranking 15th at the official false detection rate (FDR) cut-off of 0.05 for identifying statistically significant DM in the 6 benchmark networks. However, for stringent FDR cut-offs 0.025 and 0.01, the proposed method identified 31 (rank 9) and 16 DMIs (rank 10) respectively. From additional analysis, our proposed approach detected a total of 44 DM in the networks in comparison to 60 for the winner of DREAM Challenge. Interestingly, for several individual benchmark networks, our performance was better or competitive with the winner.

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Recursive module extraction using Louvain and PageRank

F1000Research ◽

10.12688/f1000research.15845.1 ◽

2018 ◽

Vol 7 ◽

pp. 1286

Author(s):

Dimitri Perrin ◽

Guido Zuccon

Keyword(s):

Community Detection ◽

Biological Networks ◽

Biological Network ◽

Biological Function ◽

Recursive Method ◽

Number Of Clusters ◽

Module Identification ◽

Pagerank Algorithm ◽

Disease Module

Biological networks are highly modular and contain a large number of clusters, which are often associated with a specific biological function or disease. Identifying these clusters, or modules, is therefore valuable, but it is not trivial. In this article we propose a recursive method based on the Louvain algorithm for community detection and the PageRank algorithm for authoritativeness weighting in networks. PageRank is used to initialise the weights of nodes in the biological network; the Louvain algorithm with the Newman-Girvan criterion for modularity is then applied to the network to identify modules. Any identified module with more than k nodes is further processed by recursively applying PageRank and Louvain, until no module contains more than k nodes (where k is a parameter of the method, no greater than 100). This method is evaluated on a heterogeneous set of six biological networks from the Disease Module Identification DREAM Challenge. Empirical findings suggest that the method is effective in identifying a large number of significant modules, although with substantial variability across restarts of the method.

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Adapting Community Detection Algorithms for Disease Module Identification in Heterogeneous Biological Networks

Frontiers in Genetics ◽

10.3389/fgene.2019.00164 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 12

Author(s):

Beethika Tripathi ◽

Srinivasan Parthasarathy ◽

Himanshu Sinha ◽

Karthik Raman ◽

Balaraman Ravindran

Keyword(s):

Community Detection ◽

Biological Networks ◽

Module Identification ◽

Detection Algorithms ◽

Disease Module

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Network Module Detection using Recursive Local Graph Sparsification and Clustering

10.20944/preprints201808.0421.v1 ◽

2018 ◽

Author(s):

Michael Banf

Keyword(s):

Biological Networks ◽

Network Module ◽

Identification Methods ◽

Structure Preserving ◽

Module Identification ◽

Wide Range ◽

Module Detection ◽

Disease Module ◽

Graph Sparsification ◽

Local Graph

Here we present a fast and highly scalable community structure preserving network module detection that recursively integrates graph sparsification and clustering. Our algorithm, called SparseClust, participated in the most recent DREAM community challenge on disease module identification, an open competition to comprehensively assess module identification methods across a wide range of biological networks.

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An adaptive refinement for community detection methods for disease module identification in biological networks using novel metric based on connectivity, conductance & modularity

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm.2017.8218027 ◽

2017 ◽

Author(s):

Raghvendra Mall ◽

Ehsan Ullah ◽

Khalid Kunji ◽

Halima Bensmail ◽

Michele Ceccarelli

Keyword(s):

Community Detection ◽

Biological Networks ◽

Adaptive Refinement ◽

Detection Methods ◽

Module Identification ◽

Disease Module

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Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

10.1101/786798 ◽

2019 ◽

Author(s):

Hongzhu Cui ◽

Suhas Srinivasan ◽

Dmitry Korkin

Keyword(s):

Biological Networks ◽

Molecular Mechanisms ◽

Association Studies ◽

Genetic Diseases ◽

Detection Methods ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Human Interactome ◽

Module Detection ◽

Disease Module

AbstractProgress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.

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Module Identification of Biological Networks via Graph Partition

Recent Advances in Biological Network Analysis ◽

10.1007/978-3-030-57173-3_6 ◽

2020 ◽

pp. 125-150

Author(s):

Yijie Wang

Keyword(s):

Biological Networks ◽

Graph Partition ◽

Module Identification

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The relationship between modularity and robustness in signalling networks

Journal of The Royal Society Interface ◽

10.1098/rsif.2013.0771 ◽

2013 ◽

Vol 10 (88) ◽

pp. 20130771 ◽

Cited By ~ 16

Author(s):

Tien-Dzung Tran ◽

Yung-Keun Kwon

Keyword(s):

Negative Correlation ◽

Biological Networks ◽

Boolean Network ◽

Negative Relationship ◽

Network Robustness ◽

Network Density ◽

Boolean Network Model ◽

Signalling Network ◽

Network Modularity ◽

The Relationship

Many biological networks tend to have a high modularity structural property and the dynamic characteristic of high robustness against perturbations. However, the relationship between modularity and robustness is not well understood. To investigate this relationship, we examined real signalling networks and conducted simulations using a random Boolean network model. As a result, we first observed that the network robustness is negatively correlated with the network modularity. In particular, this negative correlation becomes more apparent as the network density becomes sparser. Even more interesting is that, the negative relationship between the network robustness and the network modularity occurs mainly because nodes in the same module with the perturbed node tend to be more sensitive to the perturbation than those in other modules. This result implies that dynamically similar nodes tend to be located in the same module of a network. To support this, we show that a pair of genes associated with the same disease or a pair of functionally similar genes is likely to belong to the same module in a human signalling network.

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