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scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses
Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Hongjun Fu, Cankun Wang, Ren Qi, Qin Ma, Dong Xu
doi: https://doi.org/10.1101/2020.08.02.233569
Juexin Wang
1Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
Anjun Ma
2Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Yuzhou Chang
2Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Jianting Gong
1Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
Yuexu Jiang
1Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
Hongjun Fu
3Department of Neuroscience, The Ohio State University, Columbus, OH 43210, USA
Cankun Wang
2Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Ren Qi
2Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Qin Ma
2Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
Dong Xu
1Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
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Posted August 03, 2020.
scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses
Juexin Wang, Anjun Ma, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Hongjun Fu, Cankun Wang, Ren Qi, Qin Ma, Dong Xu
bioRxiv 2020.08.02.233569; doi: https://doi.org/10.1101/2020.08.02.233569
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