Xiaohan Chen
Tongji University
10 Papers
Xiaohan Chen is an academic researcher from Tongji University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 4 publications.
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Papers
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.
The tumor therapy landscape of synthetic lethality
Biyu Zhang,Chen Tang,Yanli Yao,Xiaohan Chen,Chi Zhou,Zhiting Wei,Feiyang Xing,Lan Chen,Xiang Cai,Zhiyuan Zhang,Shuyang Sun,Qi Liu +11 more
TL;DR: The Synthetic Lethality Knowledge Graph (SLKG) as mentioned in this paper integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of synthetic lethality and dosage lethality pairs.
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network
Xiaowen Wang,Hongming Zhu,Yizhi Jiang,Yulong Li,Chen Tang,Xiaohan Chen,Yunjie Li,Qi Li,Qin Liu +8 more
TL;DR: This study proposes a novel deep learning method, termed PRODeepSyn, for predicting anticancer synergistic drug combinations using the protein–protein interaction network with omics data and builds a deep neural network with the Batch Normalization mechanism to predict synergy scores using the cell line embeddings and drug features.
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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