BioX Future Interdisciplinary Technology Mathematics Information Life Sciences

† first authors with equal contribution, * corresponding authors

[52] R Li, Y Li, H Hua, Z Li, H Su, Y Zhang, Y Yao, Y Wang, S Chen*, Y Xu*. CSFeatures improves the identification of cell-type-specific differential features in single-cell and spatial omics data. Journal of Advanced Research, 2026, Online. [link, code]

[51] D Yu, C Li, S Yan, L Guo, J Liang, S Chen*, W Bu*. Comparative evaluation of gene selection approaches in transcriptomics: bias correction and visualization with TransPro. GigaScience, 2026, giag057. [link, code]

[50] Z Li, X Cui, X Chen, Z Gao, Y Liu, Y Pan, S Chen, H Lv, L Zhai*, R Jiang*. Cross-modality representation and multi-sample integration of spatially resolved omics data. Briefings in Bioinformatics, 2026, bbag214. [link, code]

[49] S Tang, S Li, G Zhang, A Lyu, H Li, S Chen*. Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework. Nature Communications, 2026, Online. [link, code]

[48] K Lang†, C Jia†, S Li†, Y Guo, D Hu, S Chen*. High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND. Genome Research, 2026, Online. Cover Article. [link, code]

[47] C Sun, Z Qin, M Li, Y Li, R Tang, S Chen, Y Wang, Y Liu, J Wei, J Liu. A unified framework combining linear and 3D molecular features for robust drug-protein interaction prediction. Cell Reports Methods, 2026, 6(2):101304. [link, code]

[46] Q Meng†, X Wu†, W Chen, Y Zhao, C Li, Z Wei, X Zeng, J Li, X Xi, S Chen, C Zhang, S Chen, J Li, X Wang, R Jiang, L Wei*, X Zhang*. A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis. Nature Communications, 2026, 17:2522. [link, code]

[45] S Dong†, S Tang†, D Liu*, S Chen*. DiTSim: a diffusion-transformers based single-cell ATAC-seq data simulator. Interdisciplinary Sciences: Computational Life Sciences, 2025, Online. [link, code]

[44] S Li†, S Tang†, H Ma, H Wang*, S Chen*. MethAgingDB: a comprehensive DNA methylation database for aging biology. Scientific Data, 2025, 12:1216. [link, code]

[43] C Yan†, Y Zhang†, J Feng†, H Hua, Z Ruan, Z Li, S Li, C Yan, P Li, J Liu*, Shengquan Chen*. Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning. Nature Communications, 2025, 16:6886. [link, code]

[42] S Zhu†, H Hua†, S Chen*. Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping. Nature Machine Intelligence, 2025, 7:1461–1477. [link, code]

[41] S Li†, Y Huang†, S Chen*. MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility data. Genome Biology, 2025, 26:162. [link, code]

[40] X Cui, Q Yin, Z Gao, Z Li, X Chen, H Lv, S Chen, Q Liu, W Zeng*, R Jiang*. CREATE: cell-type-specific cis-regulatory element identification via discrete embedding. Nature Communications, 2025, 16:4607. [link, code]

[39] S Li†, H Hua†, S Chen*. Graph neural networks for single-cell omics data: a review of approaches and applications. Briefings in Bioinformatics, 2025, bbaf109. [link, code]

[38] H Hua†, S Li†, H Liang, S Chen*. Facilitating single-cell chromatin accessibility research with a user-friendly database. Frontiers of Computer Science, 2025, 19(11): 1911920. [link, code]

[37] S Tang, S Li, S Chen*. Imputing not available values in single-cell DNA methylation data using the median is straightforward and effective. Quantitative Biology, 2025, e70000. [link, code]

[36] L Cao, W Zhang, F Yang, S Chen, X Huang, F Zeng*, Y Wang*. BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells. Briefings in Bioinformatics, 2025, bbaf013. [link, code]

[35] S Li†, X Zhuang†, S Jia†, S Tang, L Yan, H Hua, Y Jia, X Zhang, Y Zhang, Q Yang*, S Chen*. MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov-Arnold network and data augmentation. Protein & Cell, 2024, pwae069. [link, code]

[34] Q Jiang, X Chen, Z Gao, J Jia, S Chen, R Jiang*. CASHeart: a database of single-cells chromatin accessibility for the human heart. Quantitative Biology, 2025, e90. [link, code]

[33] S Feng, S Li, L Chen*, S Chen*. Unveiling potential threats: backdoor attacks in single-cell pretrained models. Cell Discovery, 2024, 10:122. [link, code]

[32] R Song†, X Wang†, J Zhang, S Chen*, J Zhou*. GATDE: A graph attention network with diffusion-enhanced protein-protein interaction for cancer classification. Methods, 2024, 231:70-77. [link, code]

[31] H Hua†, W Long†, Y Pan†, S Li, J Zhou*, H Wang*, S Chen*. scCrab: a reference-guided ensemble method for cancer cell identification via Bayesian neural networks. Interdisciplinary Sciences: Computational Life Sciences, 2025, 17:12-26. [link, code]

[30] Z Ruan, W Zhou, H Liu, J Wei, Y Pan, C Yan, X Wei, W Xiang, C Yan, S Chen, J Liu*. Precise detection of cell-type-specific domains in spatial transcriptomics. Cell Reports Methods, 2024, 4(8):100841. [link, code]

[29] Q Jiang†, S Chen†, X Chen, R Jiang*. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism. Bioinformatics, 2024, btae265. [link, code]

[28] Z Gao, R Jiang, S Chen*. OpenAnnotateApi: Python and R packages to efficiently annotate and analyze chromatin accessibility of genomic regions. Bioinformatics Advances, 2024, vbae055. [link, code]

[27] S Li, Y Li, Y Sun, Y Li, X Chen, S Tang, S Chen*. EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data. Bioinformatics, 2024, btae191. [link, code]

[26] Y Cao, X Zhao, S Tang, Q Jiang, S Li, S Li, S Chen*. scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders. Nature Communications, 2024, 15:2973. Editors' Highlights. [link, code]

[25] X Cui, X Chen, Z Li, Z Gao, S Chen*, R Jiang*. Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity. Nature Computational Science, 2024, 4:346-359. [link, code]

[24] Y Jia†, S Li†, R Jiang, S Chen*. Accurate annotation for differentiating and imbalanced cell types in single-cell chromatin accessibility data. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2024, 21(3):461-471. [link, code]

[23] S Tang, X Cui, R Wang, S Li, S Li, X Huang, S Chen*. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nature Communications, 2024, 15:1629. Editors' Highlights. [link, code]

[22] H Wang†, Y Wang†, Q Jiang†, Y Zhang*, S Chen*. SCREEN: predicting single-cell gene expression perturbation responses via optimal transport. Frontiers of Computer Science, 2024, 18:183909. [link, code]

[21] K Li†, X Chen†, S Song, L Hou, S Chen*, R Jiang*. Cofea: correlation-based feature selection for single-cell chromatin accessibility data. Briefings in Bioinformatics, 2024, bbad458. [link, code]

[20] W Zhang, R Jiang, S Chen*, Y Wang*. scIBD: a self-supervised iterative-optimizing model for boosting the detection of heterotypic doublets in single-cell chromatin accessibility data. Genome Biology, 2023, 24:225. [link, code]

[19] Z Gao†, X Chen†, Z Li†, X Cui†, Q Jiang, K Li, S Chen*, R Jiang*. scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data. Journal of Genetics and Genomics, 2023, 51(4):462-465. [link, tool]

[18] Z Li, X Chen, X Zhang, R Jiang, S Chen*. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics. Genome Research, 2023, 33:1757-1773. [link, code]

[17] C Li†, X Chen†, S Chen, R Jiang*, X Zhang*. simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data. Bioinformatics, 2023, btad453. [link, code]

[16] S Li, S Tang, Y Wang, S Li, Y Jia, S Chen*. Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance. Quantitative Biology, 2023, 12(1):85-99. [link, code]

[15] R Jiang, Z Li, Y Jia, S Li, S Chen*. SINFONIA: scalable identification of spatially variable genes for deciphering spatial domains. Cells, 2023, 12(4):604. [link, code]

[14] S Chen*, R Wang, W Long, R Jiang*. ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data. Bioinformatics, 2022, btac842. [link, code]

[13] Z Zhang, S Chen, Z Lin*. RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. Briefings in Bioinformatics, 2022, bbac540. [link, code]

[12] X Chen†, S Chen†, S Song, Z Gao, L Hou, X Zhang, H Lv, R Jiang*. Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nature Machine Intelligence, 2022, 4:116-126. [link, code]

[11] X Xi, H Li, S Chen, T Lv, T Ma, R Jiang, P Zhang, WH Wong*, X Zhang*. Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling. iScience, 2022, 25(8):104790. [link, code]

[10] S Chen†, G Yan†, W Zhang, J Li, Ru Jiang*, Z Lin*. RA3 is a reference-guided approach for epigenetic characterization of single cells. Nature Communications, 2021, 12:2177. [link, code]

[9] S Chen, Q Liu, X Cui, Z Feng, C Li, X Wang, X Zhang, Y Wang, R Jiang*. OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions. Nucleic Acids Research, 2021, 49(W1):W483-W490. [link, tool]

[8] W Zeng†, S Chen†, X Cui†, X Chen, Z Gao, R Jiang*. SilencerDB: a comprehensive database of silencers. Nucleic Acids Research, 2021, 49(D1):D221-D228. [link, tool]

[7] S Chen, M Gan, H Lv, R Jiang*. DeepCAPE: a deep convolutional neural network for the accurate prediction of enhancers. Genomics, Proteomics & Bioinformatics, 2021, 19(4):565-577. [link, code]

[6] S Chen, B Zhang, X Chen, X Zhang, R Jiang*. stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics. ISMB/ECCB 2021, Bioinformatics, 2021, 37(S1):i299-i307. [link, tool]

[5] Q Liu, S Chen, R Jiang*, WH Wong*. Simultaneous deep generative modeling and clustering of single cell genomic data. Nature Machine Intelligence, 2021, 3:536-544. [link, code]

[4] X Chen, S Chen, R Jiang*. EnClaSC: A novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes. BMC Bioinformatics, 2020, 21(13):1-16. [link, code]

[3] S Chen, K Hua, H Cui, R Jiang*. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data. BMC Bioinformatics, 2019, 20(7):139-151. [link, code]

[2] S Song, H Cui, S Chen, Q Liu, R Jiang*. EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information. Quantitative Biology, 2019, 7(3):233-243. [link, tool]

[1] X Min†, W Zeng†, S Chen, N Chen, T Chen, R Jiang*. Predicting enhancers with deep convolutional neural networks. BMC Bioinformatics, 2017, 18(13):478. [link, code]