Predicting enhancers with deep convolutional neural networks. - Xu Min; Wanwen Zeng; Shengquan Chen; Ning Chen 002; Ting Chen 006; Rui Jiang 001 (BMC Bioinform., 2017)

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data. - Shengquan Chen; Kui Hua; Hongfei Cui; Rui Jiang 001 (BMC Bioinform., 2019)

EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information. - Shaoming Song; Hongfei Cui; Shengquan Chen; Qiao Liu 008; Rui Jiang 001 (Quant. Biol., 2019)

Research and Improvement of Community Discovery Algorithm Based on Spark for Large Scale Complicated Networks. - Hui Kang; Shengquan Chen; Lingfeng Lu; Chenkun Meng (TrustCom, 2020)

EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes. - Xiaoyang Chen; Shengquan Chen; Rui Jiang 001 (BMC Bioinform., 2020)

stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics. - Shengquan Chen; Boheng Zhang; Xiaoyang Chen; Xuegong Zhang; Rui Jiang 001 (Bioinform., 2021)

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers. - Shengquan Chen; Mingxin Gan; Hairong Lv; Rui Jiang 001 (Genom. Proteom. Bioinform., 2021)

OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions. - Shengquan Chen; Qiao Liu 008; Xuejian Cui; Zhan-Ying Feng; Chunquan Li; Xiaowo Wang; Xuegong Zhang; Yong Wang; Rui Jiang 001 (Nucleic Acids Res., 2021)

SilencerDB: a comprehensive database of silencers. - Wanwen Zeng; Shengquan Chen; Xuejian Cui; Xiaoyang Chen; Zijing Gao; Rui Jiang 001 (Nucleic Acids Res., 2021)

Simultaneous deep generative modelling and clustering of single-cell genomic data. - Qiao Liu 008; Shengquan Chen; Rui Jiang 001; Wing Hung Wong (Nat. Mach. Intell., 2021)

Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. - Xiaoyang Chen; Shengquan Chen; Shuang Song 006; Zijing Gao; Lin Hou; Xuegong Zhang; Hairong Lv; Rui Jiang 001 (Nat. Mach. Intell., 2022)

RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. - Zheng Zhang; Shengquan Chen; Zhixiang Lin (Briefings Bioinform., 2023)

ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data. - Shengquan Chen; Rongxiang Wang; Wenxin Long; Rui Jiang (Bioinform., 2023)

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data. - Chen Li; Xiaoyang Chen; Shengquan Chen; Rui Jiang 001; Xuegong Zhang (Bioinform., 2023)

BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells. - Lan Cao; Wenhao Zhang; Fan Yang; Shengquan Chen; Xiaobing Huang; Feng Zeng; Ying Wang 005 (Briefings Bioinform., 2024)

Cofea: correlation-based feature selection for single-cell chromatin accessibility data. - Keyi Li; Xiaoyang Chen; Shuang Song 006; Lin Hou; Shengquan Chen; Rui Jiang 001 (Briefings Bioinform., 2024)

Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation. - Yuxi Li 010; Yi Liu 014; Yuekang Li; Ling Shi 002; Gelei Deng; Shengquan Chen; Kailong Wang 001 (CoRR, 2024)

Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance. - Siyu Li; Songming Tang; Yunchang Wang; Sijie Li; Yuhang Jia; Shengquan Chen (Quant. Biol., 2024)

Accurate Annotation for Differentiating and Imbalanced Cell Types in Single-Cell Chromatin Accessibility Data. - Yuhang Jia; Siyu Li; Rui Jiang 001; Shengquan Chen (IEEE ACM Trans. Comput. Biol. Bioinform., 2024)

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity. - Xuejian Cui; Xiaoyang Chen; Zhen Li; Zijing Gao; Shengquan Chen; Rui Jiang 001 (Nat. Comput. Sci., 2024)

SCREEN: predicting single-cell gene expression perturbation responses via optimal transport. - Haixin Wang; Yunhan Wang; Qun Jiang; Yan Zhang; Shengquan Chen (Frontiers Comput. Sci., 2024)

Graph neural networks for single-cell omics data: a review of approaches and applications. - Sijie Li; Heyang Hua; Shengquan Chen (Briefings Bioinform., 2025)

CASHeart: A database of single cells chromatin accessibility for the human heart. - Qun Jiang; Xiaoyang Chen; Zijing Gao; Jinmeng Jia; Shengquan Chen; Rui Jiang 001 (Quant. Biol., 2025)

Imputing not available values in single-cell DNA methylation data using the median is straightforward and effective. - Songming Tang; Siyu Li; Shengquan Chen (Quant. Biol., 2025)

Facilitating single-cell chromatin accessibility research with a user-friendly database. - Heyang Hua; Sijie Li; Haitian Liang; Shengquan Chen (Frontiers Comput. Sci., 2025)