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scMM: Mixture-of-experts multimodal deep generative model for single-cell multiomics data analysis
View ORCID ProfileKodai Minoura, Ko Abe, Hyunha Nam, Hiroyoshi Nishikawa, Teppei Shimamura
doi: https://doi.org/10.1101/2021.02.18.431907
Kodai Minoura
1Department of Systems Biology, Department of Immunology, Nagoya University, Graduate School of Medicine, Nagoya, Japan.
Ko Abe
2Department of Systems Biology, Nagoya University, Graduate School of Medicine, Nagoya, Japan
Hyunha Nam
2Department of Systems Biology, Nagoya University, Graduate School of Medicine, Nagoya, Japan
Hiroyoshi Nishikawa
3Department of Immunology, Nagoya University, Graduate School of Medicine, Nagoya, Japan
4Division of Cancer Immunology, Research Institute/EPOC, National Cancer Center, Tokyo/Chiba, Japan
Teppei Shimamura
5Department of Systems Biology, Nagoya University, Graduate School of Medicine, Nagoya, Japan.
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Posted March 02, 2021.
scMM: Mixture-of-experts multimodal deep generative model for single-cell multiomics data analysis
Kodai Minoura, Ko Abe, Hyunha Nam, Hiroyoshi Nishikawa, Teppei Shimamura
bioRxiv 2021.02.18.431907; doi: https://doi.org/10.1101/2021.02.18.431907
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