RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart

Liu, Wei; Sun, Xingen; Zhou, Lili; Lin, Hui; Jiang, Yi

doi:10.3389/fgene.2020.591461

Cited by 9 publications

(5 citation statements)

References 57 publications

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“…An improved Markov blanket discovery algorithm based on IMBDANET has been proposed and can effectively distinguish between direct and indirect regulatory genes from GN and reduce the false-positive rate in the network inference process 36 . Additionally, RWRNET is an algorithm of Random Walk with Restart (RWR) modified by restart probability, initial probability vector, and roaming network applied to GRN that continuously maps the global topology of the network and estimates the affinity between nodes in the network through circular iterations until all nodes are traversed 37 . In contrast, IMBDANET uses a Markov blanket discovery algorithm for network topology analysis and processing, identifying direct and indirect regulatory genes while solving the problem of isolated nodes.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs

Liang

Wei

Hou

et al. 2022

Sci Rep

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Acute-on-chronic liver failure (ACLF) is a critical and refractory disease and a hepatic disorder accompanied by immune dysfunction. Thus, it is essential to explore key immune-related genes of ACLF and investigate its mechanisms. We used two public datasets (GSE142255 and GSE168048) to perform various bioinformatics analyses, including WGCNA, CIBERSORT, and GSEA. We also constructed an ACLF immune-related protein–protein interaction (PPI) network to obtain hub differentially expressed genes (DEGs) and predict corresponding miRNAs. Finally, an ACLF rat model was established to verify the results. A total of 388 DEGs were identified in ACLF, including 162 upregulated and 226 downregulated genes. The enrichment analyses revealed that these DEGs were mainly involved in inflammatory-immune responses and biosynthetic metabolic pathways. Twenty-eight gene modules were obtained using WGCNA and the coral1 and darkseagreen4 modules were highly correlated with M1 macrophage polarization. As a result, 10 hub genes and 2 miRNAs were identified to be significantly altered in ACLF. The bioinformatics analyses of the two datasets presented valuable insights into the pathogenesis and screening of hub genes of ACLF. These results might contribute to a better understanding of the potential molecular mechanisms of ACLF. Finally, further studies are required to validate our current findings.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs

Liang

Wei

Hou

et al. 2022

Sci Rep

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“…Gephi 0.9.2 [53] was used to visualize networks. Here network proximity [54] and RWR network algorithm [55] was used to find the HPV-host associated key proteins from the HPV2C network. To identify significant proteins for each of the network RWR algorithms, permutation tests were carried out 10,000 times.…”

Section: Methodsmentioning

confidence: 99%

Network-based drug repurposing for HPV-associated cervical cancer

Ahmed,

Yang,

Samantasinghar

et al. 2023

Computational and Structural Biotechnology Journal

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“…Weighted PageRank intensifies the weights of interactions using an efficient parameter (e.g. weight-reinforcement rate parameter) to modularise the network [39], [40]. Meanwhile, RWR naively considers the original interaction weights based on reliability scores in the PPI network [41]- [43], gene ontology based on the similarity of genes [44] or the relationship between heterogeneous biomedical concepts [45] for network construction.…”

Section: 𝑣 ∈𝐵(𝑢)mentioning

confidence: 99%

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

Hui,

Kasim,

Fudzee

et al. 2023

IEEE Access

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Traditional techniques for identifying disease genes and disease modules involve high-cost clinical experiments and unpredictable time consumption for analysis. Network-based computational approaches usually focus on the systematic study of molecular networks to predict the associations between diseases and genes. The random walk-based method is a network-based approach that utilises biological networks for analysis. As the random walk models efficiently capture the complex interplay among molecules in diseases, it is extensively applied in biological problem-solving based on networks. Despite their comprehensive employment, the fundamentals of random walk and overall background may not be fully understood, leading to misinterpretation of results. This review aims to cover the fundamental knowledge of random walk models for biological network analysis. This study reviewed diffusion-based random walk methods for disease gene prediction and disease module identification. The random walk-based disease gene prediction methods are categorised into node classification and link prediction tasks. This study details the advantages and limitations of each method. Finally, the potential challenges and research directions for future studies on random walk models are highlighted.

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RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart

Cited by 9 publications

References 57 publications

Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs

Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs

Network-based drug repurposing for HPV-associated cervical cancer

A Review of Random Walk-Based Method for the Identification of Disease Genes and Disease Modules

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