ArticleBMC bioinformatics2022
RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From genes to germ layers: virtual twins of gastruloids.NPJ systems biology and applications · 2026Review
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.Bioinformatics (Oxford, England) · 2024Article
- STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data.Bioinformatics (Oxford, England) · 2023Article
- Comparative network analysis reveals the dynamics of organic acid diversity during fruit ripening in peach (Prunus persica L. Batsch).BMC plant biology · 2023Article
- KISL: knowledge-injected semi-supervised learning for biological co-expression network modules.Frontiers in genetics · 2023Article
- Dynamic Network Biomarker Analysis Reveals the Critical Phase Transition of Fruit Ripening in Grapevine.Genes · 2022Article
- Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes.Frontiers in genetics · 2022Article
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2 authors.
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Abstract
backgroundCurrent gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs.
resultsTo assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data.
conclusionsIn the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.
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