ArticleFrontiers in genetics2020
RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- HSTXGB: a hyperparameter self-tuning XGBoost method integrating pre- and post-processing for gene regulatory network inference.Frontiers in genetics · 2026Article
- RNA stability: a novel perspective on gene regulatory networks in bovine mastitis.BMC genomics · 2025Article
- Inferring independent sets of Gaussian variables after thresholding correlations.Journal of the American Statistical Association · 2025Article
- Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules.BME frontiers · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
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- Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology.Chinese herbal medicines · 2024Review
- AGRN: accurate gene regulatory network inference using ensemble machine learning methods.Bioinformatics advances · 2023Article
- Network-based drug repurposing for HPV-associated cervical cancer.Computational and structural biotechnology journal · 2023Article
- Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs.Scientific reports · 2022Article
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6 authors.
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Abstract
Inferring gene regulatory networks from expression data is essential in identifying complex regulatory relationships among genes and revealing the mechanism of certain diseases. Various computation methods have been developed for inferring gene regulatory networks. However, these methods focus on the local topology of the network rather than on the global topology. From network optimisation standpoint, emphasising the global topology of the network also reduces redundant regulatory relationships. In this study, we propose a novel network inference algorithm using Random Walk with Restart (RWRNET) that combines local and global topology relationships. The method first captures the local topology through three elements of random walk and then combines the local topology with the global topology by Random Walk with Restart. The Markov Blanket discovery algorithm is then used to deal with isolated genes. The proposed method is compared with several state-of-the-art methods on the basis of six benchmark datasets. Experimental results demonstrated the effectiveness of the proposed method.
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