Miscell: An efficient self-supervised learning approach for dissecting single-cell transcriptome

Miscell: An efficient self-supervised learning approach for dissecting single-cell transcriptome

Summary: We developed Miscell, a self-supervised learning approach with deep neural network as latent feature encoder for mining information from single-cell transcriptomes. We demonstrated the capability of Miscell with canonical single-cell analysis tasks including delineation of single-cell clust...

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Détails bibliographiques
Auteurs principaux: Hongru Shen, Yang Li, Mengyao Feng, Xilin Shen, Dan Wu, Chao Zhang, Yichen Yang, Meng Yang, Jiani Hu, Jilei Liu, Wei Wang, Qiang Zhang, Fangfang Song, Jilong Yang, Kexin Chen, Xiangchun Li
Format: article
Langue:EN
Publié: Elsevier 2021
Sujets:
Biological sciences
Neural networks
Transcriptomics
Science
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Accès en ligne:https://doaj.org/article/ae5873effb654cd6beaee306458de941
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https://doaj.org/article/ae5873effb654cd6beaee306458de941

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