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Shengquan Chen

Showing results (1-10 of 56) with videos related to

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American Journal of Clinical Pathology|October 22, 2024
Regarding the predictors of clinical outcome in myeloproliferative neoplasm, unclassifiableShengquan Chen, Chunyang Zhou
Quantitative Biology (Beijing, China)|February 12, 2026
Imputing not available values in single-cell DNA methylation data using the median is straightforward and effectiveSongming Tang, Siyu Li, Shengquan Chen
Briefings in Bioinformatics|December 13, 2022
RefTM: reference-guided topic modeling of single-cell chromatin accessibility dataZheng Zhang, Shengquan Chen, Zhixiang Lin
BMC Bioinformatics|September 17, 2020
EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomesXiaoyang Chen, Shengquan Chen, Rui Jiang
Genome Biology|June 11, 2025
MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility dataSiyu Li, Yifan Huang, Shengquan Chen
Briefings in Bioinformatics|March 17, 2025
Graph neural networks for single-cell omics data: a review of approaches and applicationsSijie Li, Heyang Hua, Shengquan Chen
Bioinformatics Advances|April 22, 2024
OpenAnnotateApi: Python and R packages to efficiently annotate and analyze chromatin accessibility of genomic regionsZijing Gao, Rui Jiang, Shengquan Chen
Bioinformatics (Oxford, England)|January 7, 2023
ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility dataShengquan Chen, Rongxiang Wang, Wenxin Long, et al.
Genomics, Proteomics & Bioinformatics|February 13, 2021
DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of EnhancersShengquan Chen, Mingxin Gan, Hairong Lv, et al.
Bioinformatics (Oxford, England)|April 16, 2024
scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanismQun Jiang, Shengquan Chen, Xiaoyang Chen, et al.
Pageof 6

Showing results (1-10 of 56) with videos related to

Sort By:
Pageof 6
American Journal of Clinical Pathology|October 22, 2024
Regarding the predictors of clinical outcome in myeloproliferative neoplasm, unclassifiableShengquan Chen, Chunyang Zhou
Quantitative Biology (Beijing, China)|February 12, 2026
Imputing not available values in single-cell DNA methylation data using the median is straightforward and effectiveSongming Tang, Siyu Li, Shengquan Chen
Briefings in Bioinformatics|December 13, 2022
RefTM: reference-guided topic modeling of single-cell chromatin accessibility dataZheng Zhang, Shengquan Chen, Zhixiang Lin
BMC Bioinformatics|September 17, 2020
EnClaSC: a novel ensemble approach for accurate and robust cell-type classification of single-cell transcriptomesXiaoyang Chen, Shengquan Chen, Rui Jiang
Genome Biology|June 11, 2025
MINGLE: a mutual information-based interpretable framework for automatic cell type annotation in single-cell chromatin accessibility dataSiyu Li, Yifan Huang, Shengquan Chen
Briefings in Bioinformatics|March 17, 2025
Graph neural networks for single-cell omics data: a review of approaches and applicationsSijie Li, Heyang Hua, Shengquan Chen
Bioinformatics Advances|April 22, 2024
OpenAnnotateApi: Python and R packages to efficiently annotate and analyze chromatin accessibility of genomic regionsZijing Gao, Rui Jiang, Shengquan Chen
Bioinformatics (Oxford, England)|January 7, 2023
ASTER: accurately estimating the number of cell types in single-cell chromatin accessibility dataShengquan Chen, Rongxiang Wang, Wenxin Long, et al.
Genomics, Proteomics & Bioinformatics|February 13, 2021
DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of EnhancersShengquan Chen, Mingxin Gan, Hairong Lv, et al.
Bioinformatics (Oxford, England)|April 16, 2024
scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanismQun Jiang, Shengquan Chen, Xiaoyang Chen, et al.
Pageof 6