Author

Han Wen

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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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Assay:proteomics· 2 Research Focus:trajectory inference· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1

Frequent Co-Authors

Yongge LiiD· 3 shared ChenChen Miao· 1 shared Cheng ChangiD· 1 shared Chunyan TianiD· 1 shared Dali HaniD· 1 shared E WeinaniD· 1 shared Feifan Zhang· 1 shared Fuchu He· 1 shared

Activity by Year

2026-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersBioinformatics

Yongge LiORCID

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Author profile →

3 papers

Recent·Most cited

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

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

πDIA-CLIP: efficient identification of highly heterogeneous proteomics data via a generalized zero-shot framework

Yucheng Liao, Yongge Li, Zexu Xiao, ChenChen Miao, Tao Yi, Xingpu Zhao, Yuanyuan Zhang, Han Wen, E Weinan, Cheng Chang, Weijie Zhang

The paper introduces πDIA-CLIP, a zero-shot framework for DIA proteomics data analysis that improves protein identification and reduces false positives across diverse datasets.

bioRxiv · Jun 21, 2026 · PreprintBioinformatics