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  • Zhiyuan YUAN
  • Research Direction：Bioinformatics, Deep Learning, Single-cell and Spatial Omics
  • Email：zhiyuan@fudan.edu.cn
  • Website：http://yuanst.fddf.xyz/
  • Brief Introduction：Received Ph.D. from the Department of Automation, Tsinghua University in June 2022, under the supervision of internationally renowned systems biologist Professor Michael Q. Zhang. Joined the Institute of Brain-Inspired Intelligence Science and Technology at Fudan University in September 2022. Selected for National Youth Talent Program, Zhou Chuan Award, and Shanghai Oriental Scholars Program. Research focuses on machine/deep learning methods for single-cell/spatial omics, with first/corresponding author publications in Nature Methods (2021/2023/2024), Nature Communications (2022/2024), Nature Protocols (2024), and Cell Genomics (2024). Several works were featured as highlight papers in Nature Methods/Nature Communications/Cell Genomics, Nature Methods cover story, Nature Methods TOP 1% paper, and ESI highly cited papers. First-authored works received China's Top 10 Advances in Bioinformatics (twice) and World Artificial Intelligence Conference Young Scholar Excellence Paper Award. Principal investigator for NSFC General Program and Youth Program grants, and key participant in National Key R&D Program and Shanghai Key Special Projects. Academic Services: Editorial Board/Young Editorial Board Member: Genomics Proteomics Bioinformatics Journal of Genetics and Genomics Advanced Biotechnology Reviewer for: Nature Methods, Nature Biotechnology, Nature Metabolism, Nature Machine Intelligence, Nature Structural & Molecular Biology, Nature Communications, Science Advances, Cell Systems, Cell Reports, Cell Reports Methods, Genome Biology, Genome Medicine, Genome Research, National Science Review, Nucleic Acids Research, Protein & Cell, BIB, GPB, JGG, Bioinformatics, CSBJ, etc.
  • Achievement：

    [1] Z Yuan*, Q Zhou*, L Cai*, et al., Y Chen#, X Zhang# & MQ Zhang#.

    SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment.

    Nature Methods 18, 1223–1232 (2021).


    [2] Z Yuan*#, W Pan*, X Zhao, F Zhao, X Li, Y Zhao, MQ Zhang# & J Yao#.

    SODB facilitates comprehensive exploration of spatial omics data.

    Nature Methods 20, 387–399 (2023).


    [3] Z Yuan*#, F Zhao, S Lin, Y Zhao, J Yao, Y Cui, XY Zhang, Y Zhao#.

    Benchmarking spatial clustering methods with spatially resolved transcriptomics data.

    Nature Methods (2024).


    [4] Z Yuan*#, Y Li*, M Shi, F Yang, J Gao, J Yao# & MQ Zhang#.

    SOTIP is a versatile method for microenvironment modeling with spatial omics data.

    Nature Communications 13, 7330 (2022).


    [5] Z Yuan*#.

    MENDER: Fast and scalable tissue structure identification in Spatial Omics Data.

    Nature Communications 15, 207 (2024).


    [6] S Lin, F Zhao, Z Wu, J Yao, Y Zhao# & Z Yuan#.

    Streamlining spatial omics data analysis with Pysodb.

    Nature Protocols (2024).


    [7] S Lin, Y Cui, F Zhao, et al., BZ Qian, Y Zhao#, Z Yuan#.

    Complete spatially resolved gene expression is not necessary for identifying spatial domains.

    Cell Genomics (2024).


    [8] Z Yuan*# & J Yao#.

    Harnessing Computational Spatial Omics to Explore the Spatial Biology Intricacies.

    Seminars in Cancer Biology (2023).


    [9] T Guo*, Z Yuan*, Y Pan, J Wang, F Chen, MQ Zhang#, X Li#.

    SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies.

    Genome Biology 24, 241 (2023).

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