Module Identification of Biological Networks via Graph Partition

2020 ◽  
pp. 125-150
Author(s):  
Yijie Wang
Keyword(s):  
Biological Networks ◽  
Graph Partition ◽  
Module Identification

scholarly journals Identifying communities from multiplex biological networks by randomized optimization of modularity

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1042 ◽  
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.

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

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 378 ◽  
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.

scholarly journals Recursive module extraction using Louvain and PageRank

F1000Research ◽  
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.

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

2019 ◽  
Vol 10 ◽  
Author(s):  
Beethika Tripathi ◽  
Srinivasan Parthasarathy ◽  
Himanshu Sinha ◽  
Karthik Raman ◽  
Balaraman Ravindran
Keyword(s):  
Community Detection ◽  
Biological Networks ◽  
Module Identification ◽  
Detection Algorithms ◽  
Disease Module

scholarly journals MUMI: Multitask Module Identification for Biological Networks

2020 ◽  
Vol 24 (4) ◽  
pp. 765-776 ◽  
Author(s):  
Weiqi Chen ◽  
Zexuan Zhu ◽  
Shan He
Keyword(s):  
Biological Networks ◽  
Module Identification

scholarly journals Network Module Detection using Recursive Local Graph Sparsification and Clustering

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.

scholarly journals Identifying communities from multiplex biological networks by randomized optimization of modularity

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1042 ◽  
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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