Author

Feifan Zhang

Chinese Academy of Sciences

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· 3

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Approach:deep learning· 3 Organism:human· 3 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Dali HaniD· 2 shared Yawei ZhangiD· 2 shared Boyuan MeiiD· 1 shared Caroline Johnson· 1 shared Chang Liu· 1 shared Chuchu WangiD· 1 shared Guanghao LiangiD· 1 shared Guoping Peng· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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2 shared papersBioinformatics

Dali HanORCID

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3 papers

Recent·Most cited

FeatureMAP enables network-guided interpretation of untargeted metabolomics

Yuting Wang, Feifan Zhang, Guoping Peng, Lifeng Qiu, Li Zeng, Yijiang Liu, Jiramet Kinchagawat, Xicheng Yang, Chang Liu, Caroline Johnson, Qi Su, Chuchu Wang, Xiaotao Shen

FeatureMAP improves interpretation of untargeted metabolomics data by integrating annotation, network refinement, and functional analysis, revealing metabolic patterns in Alzheimer's disease.

bioRxiv · Nov 5, 2026 · PreprintBioinformatics

IDEAL-Age: an interpretable deep learning framework for single-cell resolution profiling of immunological aging

Yin Xu, Zhengchao Luo, Kai He, Feifan Zhang, Yawei Zhang, Jinzhuo Wang, Han Wen, Yongge Li, Dali Han

IDEAL-Age is a deep learning framework that profiles immunological aging at single-cell resolution, showing improved predictive performance and revealing cellular contributions to aging processes.

Genome biology · Jul 26, 2026Peer-reviewedBioinformatics

Ultra-precision deconvolution of spatial transcriptomics decodes immune heterogeneity and fate-defining programs in tissues

Yin Xu, Zurui Huang, Yawei Zhang, Minghui Gong, Zhenghang Wang, Peijin Guo, Feifan Zhang, Jing Yang, Guanghao Liang, Lihui Dong, Renbao Chang, Yu Xia, Haochen Ni, Wenxuan Gong, Boyuan Mei, , Dali Han

The paper introduces UCASpatial, an ultra-precision spatial transcriptomics deconvolution algorithm, to improve the identification of immune cell subpopulations and their functional roles in tissue microenvironments.

Nature Communications · Mar 20, 2026Peer-reviewedBioinformatics