cscGAN learns non-linear gene-gene dependencies from cell type samples in order to generate realistic cells of defined types. Long Description (required)
cscGAN learns non-linear gene-gene dependencies from cell type samples in order to generate realistic cells of definitely types. Augmenting sparse cell populations improves the detection of marker genes, the robustness of and reliability of classifiers as well as the assessment of novel analysis algorithms. machine-learning, Generative Adversarial Neural Network, https://github.com/imsb-uke/scGAN u241918@bcm.edu
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Summary of Proposed Changes Current Citations/Applications
[Pubmed ID: 31919373 ],
Marouf M, Machart P, Bansal V, Kilian C, Magruder DS, Krebs CF, Bonn S ,
Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. ,
Nat Commun ,
01-09-2020 ,
https://www.ncbi.nlm.nih.gov/pubmed/?term=31919373, Primary Citation
[Pubmed ID: 35179571 ],
Heydari AA, Davalos OA, Zhao L, Hoyer KK, Sindi SS ,
ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders. ,
Bioinformatics ,
04-12-2022 ,
https://www.ncbi.nlm.nih.gov/pubmed/?term=35179571, , Application
[Pubmed ID: 36912104 ],
Liu C, Huang H, Yang P ,
Multi-task learning from multimodal single-cell omics with Matilda. ,
Nucleic Acids Res ,
05-08-2023 ,
https://www.ncbi.nlm.nih.gov/pubmed/?term=36912104, , Application