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August 19, 2025Open Access

Causal single-cell RNA-seq simulation, in silico perturbation, and GRN inference benchmarking using GRouNdGAN-Toolkit

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Authors

YZYazdan ZinatiAEAmin Emad

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Overview

Causal single-cell RNA-seq simulation improves gene regulatory network inference, suggesting new methodologies for validation.

Key Points

  • Causal inference improves GRN accuracy, aiding researchers in understanding genetic regulations in cells.
  • The GRouNdGAN-Toolkit provides customizable synthetic data generation for benchmarking GRN inference methods.
  • This toolkit builds upon existing simulation frameworks, offering additional analysis capabilities and user-friendly options.
  • The research supports the integration of simulation tools in a wider range of biological studies, enhancing experimental design.

Cite This Study

Zinati et al. (2025) studied this question.

synapsesocial.com/papers/68af35efcf1dd9ea359ea6echttps://doi.org/10.1101/2025.08.14.670294
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Also Consider

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  1. 1GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models2025
  2. 2AutoGRN: An Automated Graph Neural Network Framework for Gene Regulatory Network Inference2025 · 3 citations
  3. 3A tool for modeling gene regulatory networks (GRN_modeler) and its applications to synthetic biology2025 · 3 citations
  4. 4Integrating Machine Learning and Biological Context for Single-Cell Gene Regulatory Network Inference2025
  5. 5MultiGRNFormer: A Transformer-Based Multi-Omics GRN Inference Framework2025