BioX Future Interdisciplinary Technology Mathematics Information Life Sciences

† first authors with equal contribution, * corresponding authors

[47] Kehan Lang†, Chenyang Jia†, Siyu Li†, Yi Guo, Dingjun Hu, Shengquan Chen*. Biologically faithful bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND. Genome Research, 2026, Accepted. [link, code]

[46] Qiuchen Meng†, Xinze Wu†, Wenchang Chen, Yubo Zhao, Chen Li, Zheng Wei, Xiaocheng Zeng, Jiaqi Li, Xi Xi, Sijie Chen, Catherine Zhang, Shengquan Chen, Jiaqi Li, Xiaowo Wang, Rui Jiang, Lei Wei*, Xuegong Zhang*. A generic reference defined by consensus peaks for single-cell ATAC-seq data analysis. Nature Communications, 2026, Online. [link, code]

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

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

[43] Chengwei Yan†, Yu Zhang†, Jiuxin Feng†, Heyang Hua, Zhihan Ruan, Zhen Li, Siyu Li, Chaoyang Yan, Pingjing Li, Jian Liu*, Shengquan Chen*. Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning. Nature Communications, 2025, 16:6886. [link, code]

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

[41] Siyu Li†, Yifan Huang†, Shengquan 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] Xuejian Cui, Qijin Yin, Zijing Gao, Zhen Li, Xiaoyang Chen, Hairong Lv, Shengquan Chen, Qiao Liu, Wanwen Zeng*, Rui Jiang*. CREATE: cell-type-specific cis-regulatory element identification via discrete embedding. Nature Communications, 2025, 16:4607. [link, code]

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

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

[37] Songming Tang, Siyu Li, Shengquan 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] Lan Cao, Wenhao Zhang, Fan Yang, Shengquan Chen, Xiaobing Huang, Feng Zeng*, Ying 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] Siyu Li†, Xinhao Zhuang†, Songbo Jia†, Songming Tang, Liming Yan, Heyang Hua, Yuhang Jia, Xuelin Zhang, Yan Zhang, Qingzhu Yang*, Shengquan 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] Qun Jiang, Xiaoyang Chen, Zijing Gao, Jinmeng Jia, Shengquan Chen, Rui Jiang*. CASHeart: a database of single-cells chromatin accessibility for the human heart. Quantitative Biology, 2025, e90. [link, code]

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

[32] Ruike Song†, Xiaofeng Wang†, Jiahao Zhang, Shengquan Chen*, Jianyu Zhou*. GATDE: A graph attention network with diffusion-enhanced protein-protein interaction for cancer classification. Methods, 2024, 231:70-77. [link, code]

[31] Heyang Hua†, Wenxin Long†, Yan Pan†, Siyu Li, Jianyu Zhou*, Haixin Wang*, Shengquan 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] Zhihan Ruan, Weijun Zhou, Hong Liu, Jinmao Wei, Yichen Pan, Chaoyang Yan, Xiaoyi Wei, Wenting Xiang, Chengwei Yan, Shengquan Chen, Jian Liu*. Precise detection of cell-type-specific domains in spatial transcriptomics. Cell Reports Methods, 2024, 4(8):100841. [link, code]

[29] Qun Jiang†, Shengquan Chen†, Xiaoyang Chen, Rui Jiang*. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism. Bioinformatics, 2024, btae265. [link, code]

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

[27] Sijie Li, Yuxi Li, Yu Sun, Yaru Li, Xiaoyang Chen, Songming Tang, Shengquan Chen*. EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data. Bioinformatics, 2024, btae191. [link, code]

[26] Yichuan Cao, Xiamiao Zhao, Songming Tang, Qun Jiang, Sijie Li, Siyu Li, Shengquan 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] Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen*, Rui 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] Yuhang Jia†, Siyu Li†, Rui Jiang, Shengquan 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] Songming Tang, Xuejian Cui, Rongxiang Wang, Sijie Li, Siyu Li, Xin Huang, Shengquan Chen*. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nature Communications, 2024, 15:1629. Editors' Highlights. [link, code]

[22] Haixin Wang†, Yunhan Wang†, Qun Jiang†, Yan Zhang*, Shengquan Chen*. SCREEN: predicting single-cell gene expression perturbation responses via optimal transport. Frontiers of Computer Science, 2024, 18:183909. [link, code]

[21] Keyi Li†, Xiaoyang Chen†, Shuang Song, Lin Hou, Shengquan Chen*, Rui Jiang*. Cofea: correlation-based feature selection for single-cell chromatin accessibility data. Briefings in Bioinformatics, 2024, bbad458. [link, code]

[20] Wenhao Zhang, Rui Jiang, Shengquan Chen*, Ying 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] Zijing Gao†, Xiaoyang Chen†, Zhen Li†, Xuejian Cui†, Qun Jiang, Keyi Li, Shengquan Chen*, Rui 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] Zhen Li, Xiaoyang Chen, Xuegong Zhang, Rui Jiang, Shengquan Chen*. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics. Genome Research, 2023, 33:1757-1773. [link, code]

[17] Chen Li†, Xiaoyang Chen†, Shengquan Chen, Rui Jiang*, Xuegong Zhang*. simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data. Bioinformatics, 2023, btad453. [link, code]

[16] Siyu Li, Songming Tang, Yunchang Wang, Sijie Li, Yuhang Jia, Shengquan 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] Rui Jiang, Zhen Li, Yuhang Jia, Siyu Li, Shengquan Chen*. SINFONIA: scalable identification of spatially variable genes for deciphering spatial domains. Cells, 2023, 12(4):604. [link, code]

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

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

[12] Xiaoyang Chen†, Shengquan Chen†, Shuang Song, Zijing Gao, Lin Hou, Xuegong Zhang, Hairong Lv, Rui Jiang*. Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. Nature Machine Intelligence, 2022, 4:116-126. [link, code]

[11] Xi Xi, Haochen Li, Shengquan Chen, Tingting Lv, Tianxing Ma, Rui Jiang, Ping Zhang, Wing Hung Wong*, Xuegong Zhang*. Unfolding the genotype-to-phenotype black box of cardiovascular diseases through cross-scale modeling. iScience, 2022, 25(8):104790. [link, code]

[10] Shengquan Chen†, Guanao Yan†, Wenyu Zhang, Jinzhao Li, Rui Jiang*, Zhixiang Lin*. RA3 is a reference-guided approach for epigenetic characterization of single cells. Nature Communications, 2021, 12:2177. [link, code]

[9] Shengquan Chen, Qiao Liu, Xuejian Cui, Zhanying Feng, Chunquan Li, Xiaowo Wang, Xuegong Zhang, Yong Wang, Rui Jiang*. OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions. Nucleic Acids Research, 2021, 49(W1):W483-W490. [link, tool]

[8] Wanwen Zeng†, Shengquan Chen†, Xuejian Cui†, Xiaoyang Chen, Zijing Gao, Rui Jiang*. SilencerDB: a comprehensive database of silencers. Nucleic Acids Research, 2021, 49(D1):D221-D228. [link, tool]

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

[6] Shengquan Chen, Boheng Zhang, Xiaoyang Chen, Xuegong Zhang, Rui 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] Qiao Liu, Shengquan Chen, Rui Jiang*, Wing Hong Wong*. Simultaneous deep generative modeling and clustering of single cell genomic data. Nature Machine Intelligence, 2021, 3:536-544. [link, code]

[4] Xiaoyang Chen, Shengquan Chen, Rui 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] Shengquan Chen, Kui Hua, Hongfei Cui, Rui Jiang*. VPAC: Variational projection for accurate clustering of single-cell transcriptomic data. BMC Bioinformatics, 2019, 20(7):139-151. [link, code]

[2] Shaoming Song, Hongfei Cui, Shengquan Chen, Qiao Liu, Rui 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] Xu Min†, Wanwen Zeng†, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang*. Predicting enhancers with deep convolutional neural networks. BMC Bioinformatics, 2017, 18(13):478. [link, code]