scholarly journals A framework for stability‐based module detection in correlation graphs

2021 ◽  
Author(s):  
Mingmei Tian ◽  
Rachael Hageman Blair ◽  
Lina Mu ◽  
Matthew Bonner ◽  
Richard Browne ◽  
...  
Keyword(s):  
Module Detection

scholarly journals ModularBoost: an efficient network inference algorithm based on module decomposition

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinyu Li ◽  
Wei Zhang ◽  
Jianming Zhang ◽  
Guang Li
Keyword(s):  
Network Inference ◽  
Detection Methods ◽  
Inference Problem ◽  
Topological Constraints ◽  
Inference Algorithms ◽  
Module Detection ◽  
Series Expression ◽  
Gene Modules ◽  
Inference Methods ◽  
Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

scholarly journals Network Bioinformatics Analysis Provides Insight into Drug Repurposing for COVID-2019

2020 ◽  
Author(s):  
Xu Li ◽  
Jinchao Yu ◽  
Zhiming Zhang ◽  
Jing Ren ◽  
Alex E. Peluffo ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Drug Repurposing ◽  
Clinical Settings ◽  
Clinical Knowledge ◽  
Phase 3 ◽  
Module Detection ◽  
Novel Drug ◽  
Insight Into ◽  
Old Drugs ◽  
Significant Health

The COVID-2019 disease caused by the SARS-CoV-2 virus (aka 2019-nCoV) has raised significant health concerns in China and worldwide. While novel drug discovery and vaccine studies are long, repurposing old drugs against the COVID-2019 epidemic can help identify treatments, with known preclinical, pharmacokinetic, pharmacodynamic, and toxicity profiles, which can rapidly enter Phase 3 or 4 or can be used directly in clinical settings. In this study, we presented a novel network based drug repurposing platform to identify potential drugs for the treatment of COVID-2019. We first analysed the genome sequence of SARS-CoV-2 and identified SARS as the closest disease, based on genome similarity between both causal viruses, followed by MERS and other human coronavirus diseases. Using our AutoSeed pipeline (text mining and database searches), we obtained 34 COVID-2019-related genes. Taking those genes as seeds, we automatically built a molecular network for which our module detection and drug prioritization algorithms identified 24 disease-related human pathways, five modules and finally suggested 78 drugs to repurpose. Following manual filtering based on clinical knowledge, we re-prioritized 30 potential repurposable drugs against COVID-2019 (including pseudoephedrine, andrographolide, chloroquine, abacavir, and thalidomide) . We hope that this data can provide critical insights into SARS-CoV-2 biology and help design rapid clinical trials of treatments against COVID-2019.

scholarly journals Network diffusion on multiple-layers: current approaches and integrative analysis of Rheumatoid Arthritis data.

2017 ◽  
Author(s):  
Noemi Di Nanni ◽  
Matteo Gnocchi ◽  
Marco Moscatelli ◽  
Luciano Milanesi ◽  
Ettore Mosca
Keyword(s):  
Rheumatoid Arthritis ◽  
Protein Interactions ◽  
Single Layer ◽  
Joint Analysis ◽  
Protein Protein Interactions ◽  
Functional Protein ◽  
Detection Function ◽  
Module Detection ◽  
Rheumatoid Arthritis Data ◽  
Network Diffusion

Network Diffusion has been proposed in several applications, thanks to its ability of amplifying biological signals and prioritizing genes that may be associated with a disease. Not surprising, the success of Network Diffusion on a “single layer” led to the first approaches for the joint analysis of multi-omics data. Here, we review integrative methods based on Network Diffusion that have been proposed with several aims (e.g. patient stratification, module detection, function prediction). We used Network Diffusion to analyse, in the context of physical and functional protein-protein interactions, genetic variation, DNA methylation and gene expression data from a study on Rheumatoid Arthritis. We identified functionally related genes with multiple alterations.

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

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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