Author

Hao Cheng

Peking University

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

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Biology Disciplines

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Bioengineering· 1 Bioinformatics· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:clustering· 2 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Dong He XuiD· 2 shared Qin MaiD· 2 shared Anjun MaiD· 1 shared Baichuan Jin· 1 shared Bingjiang LyuiD· 1 shared Congcong Li· 1 shared Dongxu Li· 1 shared Fei HeiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersCell Biology

Dong He XuORCID

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3 papers

Recent·Most cited

Wearable magnetoencephalography in an open environment

Fufu Zheng, Congcong Li, Tingyue Li, Rui Yang, Wei Xu, Hao Cheng, Dongxu Li, Kaiyan He, Yugang Yin, Xingyu Ru, Bingjiang Lyu, Jia‐Hong Gao

This paper introduces a wearable OPM-MEG system that enables brain activity localization in an open environment without magnetic shielding.

bioRxiv · Jul 31, 2026 · PreprintBioengineering

Harnessing the Power of Single-Cell Large Language Models with Parameter Efficient Fine-Tuning using scPEFT

Fei He, Ruixin Fei, Jordan Edward Krull, Yang Yu, Xinyu Zhang, Xianyu Wang, Hao Cheng, Mingyue Gao, Li Su, Yibo Chen, Jinpu Li, Baichuan Jin, Yuzhou Chang, Anjun Ma, Qin Ma, Dong He Xu

This paper introduces scPEFT, a parameter-efficient fine-tuning framework for scLLMs that improves task adaptation with reduced computational costs and enhances performance in disease-specific and cross-species analyses.

Nature Machine Intelligence · Dec 31, 2025Peer-reviewedCell Biology

Exploring the Mosaic-like Tissue Architecture of Kidney Diseases Using Relation Equivariant Graph Neural Networks on Spatially Resolved Transcriptomics

Mauminah Raina, Hao Cheng, Hari Naga Sai Kiran Suryadevara, Treyden Stransfield, Dong He Xu, Qin Ma, Michael T. Eadon, Juexin Wang

This paper introduces REGNN, a graph deep learning framework for analyzing spatially resolved transcriptomics data, showing improved performance in identifying mosaic-like tissue architectures in kidney diseases.

bioRxiv · Nov 9, 2023 · PreprintBioinformatics