ArticleQuantitative biology (Beijing, China)2023
Gene regulatory network inference based on causal discovery integrating with graph neural network.
Article in Quantitative biology (Beijing, China), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Decoding neuronal gene expression: integrative insights from omics and AI.Brain informatics · 2026Review
- A comprehensive survey on graph neural networks for gene regulatory network inference.Briefings in bioinformatics · 2026Review
- A causal reinforcement learning framework for reliable gene regulatory network inference.BMC bioinformatics · 2026Article
- CAGAD: dynamic community attention for prediction gene regulatory network.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- Efficient structure learning of gene regulatory networks with Bayesian active learning.BMC bioinformatics · 2025Article
- TRENDY: gene regulatory network inference enhanced by transformer.Bioinformatics (Oxford, England) · 2025Article
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Gene regulatory network inference based on causal discovery integrating with graph neural network.Quantitative biology (Beijing, China) · 2023Article
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4 authors.
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Abstract
Gene regulatory network (GRN) inference from gene expression data is a significant approach to understanding aspects of the biological system. Compared with generalized correlation-based methods, causality-inspired ones seem more rational to infer regulatory relationships. We propose GRINCD, a novel GRN inference framework empowered by graph representation learning and causal asymmetric learning, considering both linear and non-linear regulatory relationships. First, high-quality representation of each gene is generated using graph neural network. Then, we apply the additive noise model to predict the causal regulation of each regulator-target pair. Additionally, we design two channels and finally assemble them for robust prediction. Through comprehensive comparisons of our framework with state-of-the-art methods based on different principles on numerous datasets of diverse types and scales, the experimental results show that our framework achieves superior or comparable performance under various evaluation metrics. Our work provides a new clue for constructing GRNs, and our proposed framework GRINCD also shows potential in identifying key factors affecting cancer development.
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