Author

Zhaoyu Fang

Central South University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4 Cell Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 5 Assay:scRNA-seq· 4 Research Focus:cell type annotation· 3 Research Focus:gene regulatory network inference· 3 Organism:human· 3 Research Focus:dimensionality reduction· 2 Research Focus:trajectory inference· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

Ziyang MiaoiD· 3 shared Jianhui LiniD· 2 shared Jiayuan DingiD· 2 shared Jiliang TangiD· 2 shared Min LiiD· 2 shared Pengzhen Jia· 2 shared Ruiqing ZhengiD· 2 shared Li TangiD· 1 shared

Activity by Year

2025-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersBioinformatics

Ziyang MiaoORCID

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5 papers

Recent·Most cited

scHiR learns joint cellular representations from paired single-cell transcriptome and 3D genome data

Yichao Zhao, Pengzhen Jia, Matthew C. Hill, Li Tang, Zhaoyu Fang, Ruiqing Zheng, Min Li

scHiR learns joint representations from paired scRNA-seq and scHi-C data, improving clustering and trajectory inference while recovering input signals.

bioRxiv · Nov 9, 2026 · PreprintBioinformatics

GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery

Ziyang Miao, Zhaoyu Fang, Xingyuan Shi, Yanping Zeng, Tianrui Wu, Ruiqing Zheng, Min Li

GRAVITY improves RNA velocity modeling by integrating gene regulatory networks, enabling accurate trajectory inference and discovery of terminal cell states in embryonic brain development.

bioRxiv · Feb 2, 2026 · PreprintBioinformatics

scLinguist: A pre-trained hyena-based foundation model for cross-modality translation in single-cell multi-omics

Zhaoyu Fang, Ziyang Miao, Jianhui Lin, Yuying Xie, Jiliang Tang, Jiayuan Ding, Min Li

scLinguist translates between single-cell multi-omics modalities by pretraining on unimodal data and fine-tuning on paired data, outperforming existing methods in predicting protein abundance from RNA.

bioRxiv · Sep 30, 2025 · PreprintCell Biology

SpatialMap: A Scalable Deep Learning Method for Cell Typing in Subcellular Spatial Transcriptomics

Pengzhen Jia, Yahui Long, Yichao Zhao, Ning Zhang, Zhaoyu Fang, Siqi Chen, Min Li

SpatialMap is a deep learning method for cell typing in subcellular spatial transcriptomics that outperforms existing methods and identifies novel cell types.

bioRxiv · Jul 3, 2025 · PreprintBioinformatics

Toward a privacy-preserving predictive foundation model of single-cell transcriptomics with federated learning and tabular modeling

Jiayuan Ding, Jianhui Lin, Shiyu Jiang, Yixin Wang, Ziyang Miao, Zhaoyu Fang, Jiliang Tang, Min Li, Xiaojie Qiu

This paper introduces Tabula, a privacy-preserving foundation model for single-cell transcriptomics using federated learning and tabular modeling, achieving better performance with less data and revealing regulatory logic across biological systems.

bioRxiv · Jan 6, 2025 · PreprintBioinformatics