We introduced a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality reduction and imputation of scRNA-seq data.
May 6, 2022 · We introduced a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality reduction and imputation of scRNA-seq data.
We introduced a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality reduction and imputation of scRNA-seq data. DAE- ...
We introduced a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality reduction and imputation of scRNA-seq data. DAE- ...
TL;DR: Zhang et al. as mentioned in this paper proposed a deep autoencoder network based on a two-part-gamma model (DAE-TPGM) for joint dimensionality ...
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DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data. Comput. Biol. Medicine 146: 105578 (2022). [+] ...
DAE-TPGM: A deep autoencoder network based on a two-part-gamma model for analyzing single-cell RNA-seq data · Author Picture Shuchang Zhao,; Author Picture Li ...
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We propose a customized autoencoder based on a two-part-generalized-gamma distribution (AE-TPGG) for scRNA-seq data analysis.
Oct 26, 2022 · We propose a customized autoencoder based on a two-part-generalized-gamma distribution (AE-TPGG) for scRNA-seq data analysis.
Missing: DAE- TPGM: