Dynamic Module Detection in Temporal Attributed Networks of cancers

2021 ◽  
pp. 1-1
Author(s):  
Dongyuan Li ◽  
Shuyao Zhang ◽  
Xiaoke Ma
Keyword(s):  
Module Detection ◽  
Attributed Networks

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 Jointly Learning Representations of Nodes and Attributes for Attributed Networks

2020 ◽  
Vol 38 (2) ◽  
pp. 1-32 ◽  
Author(s):  
Zaiqiao Meng ◽  
Shangsong Liang ◽  
Xiangliang Zhang ◽  
Richard McCreadie ◽  
Iadh Ounis
Keyword(s):  
Attributed Networks

Unifying community detection and network embedding in attributed networks

2021 ◽  
Vol 63 (5) ◽  
pp. 1221-1239
Author(s):  
Yu Ding ◽  
Hao Wei ◽  
Guyu Hu ◽  
Zhisong Pan ◽  
Shuaihui Wang
Keyword(s):  
Community Detection ◽  
Network Embedding ◽  
Attributed Networks

scholarly journals Differentially Private Attributed Network Releasing Based on Early Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuye Wang ◽  
Jing Yang ◽  
Jianpei Zhan
Keyword(s):  
Topological Structure ◽  
Differential Privacy ◽  
Probability Model ◽  
Prior Work ◽  
Data Utility ◽  
High Data ◽  
Early Fusion ◽  
Attributed Network ◽  
Analysis Of Social Networks ◽  
Attributed Networks

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

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.

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