Author

Yazdan Zinati

McGill UniversityORCID 0009-0005-9952-2515Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:benchmarking· 2 Research Focus:database / resource· 2 Approach:deep learning· 2 Research Focus:gene regulatory network inference· 2 Assay:scRNA-seq· 2 Research Focus:software / tool· 2 Approach:statistical model· 2 Approach:simulation· 1

Frequent Co-Authors

Amin EmadiD· 2 shared Abdulrahman TakiddeeniD· 1 shared

Activity by Year

2024-2025 · peak 1/yr

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2 shared papersBioinformatics

Amin EmadORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintAug 14, 2025

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

Yazdan Zinati, Amin Emad

The paper introduces GRouNdGAN-Toolkit, a toolkit for simulating scRNA-seq data and benchmarking GRN inference methods using causal generative models.

DOI 10.1101/2025.08.14.670294

scRNA-seqdeep learning

𝕏 PostBluesky

BioinformaticsNature CommunicationsPeer-reviewedMay 14, 2024

GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks

Yazdan Zinati, Abdulrahman Takiddeen, Amin Emad

GRouNdGAN simulates single-cell RNA-seq data using causal generative adversarial networks guided by gene regulatory networks, outperforming existing simulators and enabling in-silico perturbation experiments.

36 citationstop 3%

scRNA-seqdeep learning

𝕏 PostBluesky