Bin Duan
Tongji University
13 Papers
Bin Duan is an academic researcher from Tongji University. The author has contributed to research in topics: Computer science & RNA-Seq. The author has an hindex of 3, co-authored 5 publications.
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Papers
DeepCRISPR: optimized CRISPR guide RNA design by deep learning
Guohui Chuai,Hanhui Ma,Jifang Yan,Ming Chen,Nanfang Hong,Dongyu Xue,Chi Zhou,Chenyu Zhu,Chen Ke,Bin Duan,Feng Gu,Sheng Qu,Deshuang Huang,Jia Wei,Qi Liu +14 more
TL;DR: DeepCRISPR is presented, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools.
Model-based understanding of single-cell CRISPR screening.
Bin Duan,Chi Zhou,Chengyu Zhu,Yifei Yu,Gaoyang Li,Shihua Zhang,Chao Zhang,Xiangyun Ye,Hanhui Ma,Shen Qu,Zhiyuan Zhang,Ping Wang,Shuyang Sun,Qi Liu +13 more
TL;DR: MUSIC provides an effective and applicable solution to elucidate perturbation function and biologic circuits by a model-based quantitative analysis of single-cell-based CRISPR screening data.
A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data
Gaoyang Li,Shaliu Fu,Shuguang Wang,Chenyu Zhu,Bin Duan,Chen Tang,Xiaohan Chen,Guohui Chuai,Ping Wang,Qi Liu +9 more
TL;DR: The single-cell multi-view profiler (scMVP) as mentioned in this paper generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification.
A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data
Gaoyang Li,Shaliu Fu,Shuguang Wang,Chenyu Zhu,Bin Duan,Chen Tang,Xiaohan Chen,Guohui Chuai,Ping Wang,Qi Liu +9 more
TL;DR: The single-cell multi-view profiler (scMVP) as mentioned in this paper generates common latent representations for dimensionality reduction, cell clustering, and developmental trajectory inference and generates separate imputations for differential analysis and cis-regulatory element identification.
Learning for single-cell assignment.
Bin Duan,Chenyu Zhu,Guohui Chuai,Chen Tang,Xiaohan Chen,Shaoqi Chen,Shaliu Fu,Gaoyang Li,Qi Liu +8 more
TL;DR: It is proved that scLearn outperformed the comparable existing methods for single-cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single- cell type identification and categorizing ability.
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