1.A User’s Guide to Foundation Models on Single-Cell and Spatial-Omics – I. Cell Types and Lineages. 2026, Under review.
2.Machine Learning-Driven Organoid Screening model (ATP-HCR) Identifies Panobinostat as a Novel Therapeutic Candidate for Liver Cancer. 2026, In submission.
3.Confinement-Driven Piezo1 Activation Induces MLKL-Mediated Necroptosis and Restricts Tumor Metastasis. 2026, Under review.
4.Zuo, C.*, Xia, J., Xu, Y., Xu, Y., Gao, P., Zhang, J., Wang, Y., Chen, L.*, stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs. Nature Communications, 2025. 16, 5317.
5.Gao, P.#, Zuo, C.#*, Yuan, W., Cai, J., Chai, X., Gong, R., Yu, J., Yao, L., Su, W., Liu, Z., Lin, S., Wang, Y., Dai, M., Ma, L., Li, Q.*, Zhou, P.*. Spatiotemporal multi-omics analysis uncovers NAD-dependent immunosuppressive niche triggering early gastric cancer. Signal Transduction and Targeted Therapy, 2025. 10: 313.
6.Jiao, M.#, Li, J.#, Zhang, M., Du, S., Zhong B., Li, S., Zhang, B., Liu, F.*, Zuo, C.*, Wang, S.*, Chen, L.*, De novo reconstruction of 3D human facial images from DNA sequence. Advanced Science, 2025. doi: 10.1002/advs.202414507.
7.Lou, Y.#, Li, X.#, Yang, Q., Dai, H., Ma K., Zuo, C.*, Vector-guided graph learning for spatial multi-slice multi-omics alignment. Cell Reports Methods, 2025, 101241.
8.Yan, Q.#, Li, X.#, Cui, J., Rong, J., Zhang J., Gao, P., Xu, Y., Qiu, F., Zuo, C.*, Spatial histology and gene-expression representation and generative learning via online self-distillation contrastive learning. Briefings in Bioinformatics, 2025. 26(4).
9.Zuo, C.*, Zhu, J., Zou, J., Chen, L.*, Unraveling tumour spatiotemporal heterogeneity using spatial multimodal data. Clinical and Translational Medicine, 2025. doi: 10.1002/ctm2.70331.
10.Wang, Y., Xie, X., Wang, Y., Sheng, N., Huang, L.*, Zuo, C.*, Supervised contrastive knowledge graph learning for ncRNA-disease association prediction. Expert Systems with Applications, 2025. 269: p. 126257.
11.Shi, M.#, Yan, Q.#, Zhao, W.#, Tang, C,#. Han, F., Chen, H., Li, Y., Xu, L., Yang, F., Yan, Z., Ren, Y., Jin, G.*, Bao, Y.*, Zuo, C.*, Li, J.*, NeoAtlas-Tumor and NeoBert: A database and A predictive Model for Canonical and Noncanonical Tumor Neoantigens. Genomics, Proteomics & Bioinformatics, 2025, qzaf105.
12.Cui, J., Gao, Y., Wang, Q., Li, X., Xu, K., Huang, Z., Zhang, J., Zuo, C.*. Advanced Cross-Graph Cycle Attention Model for Dissecting Complex Structures in Mass Spectrometry Imaging. Journal of Computer Science and Technology, 2025, doi: 10.1007/s11390-025-4342-2.
13.Rong, J.#, Zhong, H.#, Meng, Y., Jin, Q., Zhang, Y., Zuo, C.*, HLIP: A pan-cancer model for histological image analysis in clinical research using TCGA. Computational Biology and Chemistry, 2025, 108589.
14.Xu, Y., Dai, H., Feng, J., Xu, K., Wang, Q., Gao, P.*, Zuo, C.*, stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning. Frontiers in Genetics, 2025, 16, 1566675.
15.Zuo, C.*, Xia, J., Chen, L.*, Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning. Nature Communications, 2024. 15(1): p. 5057.
16.Zhang, Y.#, Zuo, C.#*, Li, Y., Liu, L., Yang, B., Xia, J., Cui, J., Xu, K., Wu, X.*, Gong, W.*, Liu, Y.*, Single-cell characterization of infiltrating T cells identifies novel targets for gallbladder cancer immunotherapy. Cancer Letters, 2024. 586: p. 216675.
17.Xia, J., Cui, J., Huang, Z., Zhang, S., Yao, F., Zhang, Y., Zuo, C.*, CellMirror: deciphering cell populations from spatial transcriptomics data by interpretable contrastive learning. IEEE International Conference on Medical Artificial Intelligence. 2023, 165-176.
18.Cui, J., Xia, J., Li, X., Wang, Y., Qiu, F., Xu, Y., Xu, K., Zuo, C.*, Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model. IEEE International Conference on Medical Artificial Intelligence. 2023, 110-121.
19.Zuo, C.*, Zhang, Y., Cao, C., Feng, J., Jiao, M., Chen, L.*, Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning. Nature Communications, 2022. 13(1): p. 5962.
20.Zhang, Y.#, Zuo, C.#, Liu, L.#, Hu, Y.#, Yang, B.#, Qiu, S., Li, Y., Cao, D., Ju, Z., Ge, J., Wang, Q., Wang, T., Bai, L., Yang, Y., Li, G., Shao, Z., Gao, Y., Li, Y., Bian, R., Miao, H., Li, L., Li, X., Jiang, C., Yan, S., Wang, Z., Wang, Z., Cui, X., Huang, W., Xiang, D., Wang, C., Li, Q., Wu, X., Gong, W., Liu, Y., Shao, R.*, Liu, F.*, Li, M.*, Chen, L.*, Liu, Y.*, Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer. Journal of Hepatology, 2021. 75(5): p. 1128-1141.
21.Xia, J., Wang, L., Zhang, G.*, Zuo, C.*, Chen, L., RDAClone: deciphering tumor heterozygosity through single-cell genomics data analysis with robust deep autoencoder. Genes, 2021, 12(12): 1847.
22.Zuo, C., and Chen, L.*, Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data. Briefings in Bioinformatics, 2021. 22(4): p. bbaa287.
23.Zuo, C., Dai, H. and Chen, L.*, Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data. Bioinformatics, 2021. 37(22): p. 4091-4099.
24.Zuo, C., Tang, Y., Fu, H., Liu, Y., Zhang, X., Zhao, B., Xu, Y.*, Elucidation and analyses of the regulatory networks of upland and lowland ecotypes of switchgrass in response to drought and salt stresses. Plos one, 2018, 13(9): e0204426.
25.Zuo, C., Blow, M., Sreedasyam, A., Kuo, A., Ramamoorthy, G., Torres-Jerez. I., Li, G., Wang, M., Dilworth, D., Barry, K., Udvardi, M., Schmutz, J., Tang, Y., Xu, Y.*, Revealing the transcriptomic complexity of switchgrass by PacBio long-read sequencing. Biotechnology for biofuels, 2018, 11(1): 170.
26.Huang, Z., Mu, X., Chen, Q., Zhong, L., Xiao, J., Zuo, C., Zhang, Y., Shi, B., Qu, Y., Tan, R., Xu, L., Guan, R., Xu, Y.*, A Model for the Development of Alzheimer’s Disease. Genomics, Proteomics & Bioinformatics, 2025, qzaf087.
27.Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2025. Nucleic Acids Research, 2025, 53(D1): D30-D44.
28.Zhang, S., Wu, X., Lian, Z., Zuo, C., Wang, Y.*, GNNMF: a multi-view graph neural network for ATAC-seq motif finding. BMC genomics, 2025, 25(1): 300.
29.Hong, J., Hou, W., Sheng, N., Zuo, C., Wang, Y. *, HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set. Expert Systems with Applications, 2025, 260: 125435.
30.Cao, C., Shao, M., Zuo, C., Kwok, D., Liu, L., Ge, Y., Zhang, Z., Cui, F., Chen, M., Fan, R., Ding, Y., Jiang, H., Wang, G., Zou, Q. *, AVAR: a curated repository for rare variant–trait associations. Nucleic Acids Research, 2024, 52(D1): D990-D997.
31.Feng, J., Zhang, S.*, Chen, L.*, Zuo, C., Alzheimer’s Disease Neuroimaging Initiative, Detection of Alzheimer’s disease using features of brain region-of-interest-based individual network constructed with the sMRI image. Computerized Medical Imaging and Graphics, 2022, 98:102057.
32.Jin, Q., Zuo, C., Cui, H., Li, L., Yang, Y., Dai, H.*, Chen, L.*, Single-cell entropy network detects the activity of immune cells based on ribosomal protein genes. Computational and Structural Biotechnology Journal, 2022, 20: 3556-3566.