Author

Chaoyang Yan

Nankai UniversityORCID 0000-0003-2061-6040Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:batch correction / integration· 1 Assay:bulk RNA-seq· 1

Frequent Co-Authors

Zhihan RuaniD· 3 shared Jian LiuiD· 2 shared Linwei LiiD· 2 shared Wenting XiangiD· 2 shared Yang Ou· 2 shared Zhidong HuangiD· 2 shared Chengwei Yan· 1 shared Heyang HuaiD· 1 shared

Activity by Year

2025-2025 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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3 shared papersPathology

Zhihan RuanORCID

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3 papers

Recent·Most cited

PathologyAdvanced SciencePeer-reviewedNov 7, 2025

Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer

Chaoyang Yan, Linwei Li, Xiaolong Qian…·Jian Liu

This paper develops CPMP, a weakly supervised transformer model, to predict MammaPrint recurrence risk from breast cancer histopathological slides, achieving an AUROC of 0.824.

1 citation

humanbulk RNA-seq

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Cancer BiologybioRxivPreprintOct 16, 2025

Atlas of Breast Cancer in Chinese Young Women Revealed by Single-cell RNA and ATAC Sequencing

Zhihan Ruan, Linwei Li, Wenting Xiang…·Hong Shu Liu

The paper establishes a single-cell atlas of breast cancer in young Chinese women, identifies new subtypes and therapeutic targets, and reveals distinct tumor progression patterns.

DOI 10.1101/2025.10.16.682857

humanscRNA-seq

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BioinformaticsNature CommunicationsPeer-reviewedJul 25, 2025

Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning

Chengwei Yan, Yu Zhang, Jiuxin Feng…·Shengquan Chen

The paper introduces cpDistiller, a method to correct three technical effects in Cell Painting data using contrastive and domain-adversarial learning, improving analysis reliability and phenotypic response detection.

4 citations

imagingcontrastive learning

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