Author

Fuchu He

Chinese Academy of Medical Sciences & Peking Union Medical College

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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Bioinformatics· 2 Biochemistry· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Assay:proteomics· 4 Approach:statistical model· 3 Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1

Frequent Co-Authors

Chuanxi Huang· 2 shared Linhai XieiD· 2 shared Yun YangiD· 2 shared Cheng ChangiD· 1 shared Chengxin DaiiD· 1 shared Chunyan TianiD· 1 shared Han Wen· 1 shared Hanbin MaiD· 1 shared

Activity by Year

2025-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersCell Biology

Chuanxi Huang

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Author profile →

4 papers

Recent·Most cited

π-MSNet: A large-scale, AI-ready and continuously reprocessed proteomics data resource

Chengxin Dai, Yi Liu, Tianze Ling, Qingyuan Zhang, Shouyang Ren, Yuan Chu, Menglu Wang, YunPing ZHU, Huali Xu, Xiaowei Huang, Timo T. Sachsenberg, Mingze Bai, Fuchu He, Yasset Pérez‐Riverol, Linhai Xie, Cheng Chang

The paper introduces π-MSNet, a large-scale proteomics dataset for AI training and benchmarking, showing performance improvements when models are retrained on it.

bioRxiv · Oct 4, 2026 · PreprintBioinformatics

MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics

Yujia Xiang, Yongge Li, Chunyan Tian, Ruichu Gu, Fuchu He, Han Wen, Linhai Xie, Peijie Zhou

MEGA-ODE reconstructs dynamic omics landscapes from sparse data, predicts unmeasured states, and prioritizes perturbations toward biological endpoints.

bioRxiv · Aug 5, 2026 · PreprintBioinformatics

High-throughput Single-cell Proteomics Enabled by Integrating nPOP-workflow with Quantitative Hyperplexing

Kunpei Cai, Qing Zeng, Chuanxi Huang, Jie Yang, Fuchu He, Yun Yang

This study develops a high-throughput single-cell proteomics workflow combining nPOP with quantitative hyperplexing, achieving high sensitivity and quantification across multiple cell types.

Analytical Chemistry · Jul 17, 2026Peer-reviewedCell Biology

High-throughput Single-cell Proteomics Enabled by an Integrated Hyperplexing and Automatic Labelling Approach without Fractionation

Hui Zhang, Qing Zeng, Kai Jin, Chuanxi Huang, Xing Liu, Jing Yang, Hanbin Ma, Fuchu He, Yun Yang

This paper introduces Integral-Hyperplex, a method combining isobaric tags and tandem mass tags to increase single-cell proteomics throughput by 30-fold, validated through quantification experiments and automated labeling.

bioRxiv · Jun 22, 2025 · PreprintBiochemistry