Shengquan Chen

According to our database, Shengquan Chen authored at least 19 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., June, 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

Nat. Comput. Sci., May, 2024

Accurate Annotation for Differentiating and Imbalanced Cell Types in Single-Cell Chromatin Accessibility Data.

Yuhang Jia, Siyu Li, Rui Jiang, Shengquan Chen

IEEE ACM Trans. Comput. Biol. Bioinform., 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

Lockpicking LLMs: A Logit-Based Jailbreak Using Token-level Manipulation.

Yuxi Li, Yi Liu, Yuekang Li, Ling Shi, Gelei Deng, Shengquan Chen, Kailong Wang

CoRR, 2024

2023

simCAS: an embedding-based method for simulating single-cell chromatin accessibility sequencing data.

Chen Li, Xiaoyang Chen, Shengquan Chen, Rui Jiang, Xuegong Zhang

Bioinform., August, 2023

ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility data.

Shengquan Chen, Rongxiang Wang, Wenxin Long, Rui Jiang

Bioinform., January, 2023

RefTM: reference-guided topic modeling of single-cell chromatin accessibility data.

Zheng Zhang, Shengquan Chen, Zhixiang Lin

Briefings Bioinform., January, 2023

2022

Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding.

Xiaoyang Chen, Shengquan Chen, Shuang Song, Zijing Gao, Lin Hou, Xuegong Zhang, Hairong Lv, Rui Jiang

Nat. Mach. Intell., 2022

2021

Simultaneous deep generative modelling and clustering of single-cell genomic data.

Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong

Nat. Mach. Intell., 2021

SilencerDB: a comprehensive database of silencers.

Wanwen Zeng, Shengquan Chen, Xuejian Cui, Xiaoyang Chen, Zijing Gao, Rui Jiang

Nucleic Acids Res., 2021

OpenAnnotate: a web server to annotate the chromatin accessibility of genomic regions.

Shengquan Chen, Qiao Liu, Xuejian Cui, Zhan-Ying Feng, Chunquan Li, Xiaowo Wang, Xuegong Zhang, Yong Wang, Rui Jiang

Nucleic Acids Res., 2021

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

Shengquan Chen, Mingxin Gan, Hairong Lv, Rui Jiang

Genom. Proteom. Bioinform., 2021

stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics.

Shengquan Chen, Boheng Zhang, Xiaoyang Chen, Xuegong Zhang, Rui Jiang

Bioinform., 2021

2020

EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomes.

Xiaoyang Chen, Shengquan Chen, Rui Jiang

BMC Bioinform., 2020

Research and Improvement of Community Discovery Algorithm Based on Spark for Large Scale Complicated Networks.

Hui Kang, Shengquan Chen, Lingfeng Lu, Chenkun Meng

Proceedings of the 19th IEEE International Conference on Trust, 2020

2019

EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information.

Shaoming Song, Hongfei Cui, Shengquan Chen, Qiao Liu, Rui Jiang

Quant. Biol., 2019

VPAC: Variational projection for accurate clustering of single-cell transcriptomic data.

Shengquan Chen, Kui Hua, Hongfei Cui, Rui Jiang

BMC Bioinform., 2019

2017

Predicting enhancers with deep convolutional neural networks.

Xu Min, Wanwen Zeng, Shengquan Chen, Ning Chen, Ting Chen, Rui Jiang

BMC Bioinform., 2017