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@ARTICLE{Marouf:145037,
      author       = {Marouf, Mohamed and Machart, Pierre and Bansal, Vikas and
                      Kilian, Christoph and Magruder, Daniel S. and Krebs,
                      Christian F. and Bonn, Stefan},
      title        = {{R}ealistic in silico generation and augmentation of
                      single-cell {RNA}-seq data using generative adversarial
                      networks.},
      journal      = {Nature Communications},
      volume       = {11},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DZNE-2020-00397},
      pages        = {166},
      year         = {2020},
      abstract     = {A fundamental problem in biomedical research is the low
                      number of observations available, mostly due to a lack of
                      available biosamples, prohibitive costs, or ethical reasons.
                      Augmenting few real observations with generated in silico
                      samples could lead to more robust analysis results and a
                      higher reproducibility rate. Here, we propose the use of
                      conditional single-cell generative adversarial neural
                      networks (cscGAN) for the realistic generation of
                      single-cell RNA-seq data. cscGAN learns non-linear gene-gene
                      dependencies from complex, multiple cell type samples and
                      uses this information to generate realistic cells of defined
                      types. Augmenting sparse cell populations with cscGAN
                      generated cells improves downstream analyses such as the
                      detection of marker genes, the robustness and reliability of
                      classifiers, the assessment of novel analysis algorithms,
                      and might reduce the number of animal experiments and costs
                      in consequence. cscGAN outperforms existing methods for
                      single-cell RNA-seq data generation in quality and hold
                      great promise for the realistic generation and augmentation
                      of other biomedical data types.},
      keywords     = {Algorithms / Animals / Biomedical Research: methods /
                      Computer Simulation / Humans / Mice / Models, Theoretical /
                      Neural Networks, Computer / RNA: genetics / RNA-Seq: methods
                      / RNA (NLM Chemicals)},
      cin          = {AG Bonn 1},
      ddc          = {500},
      cid          = {I:(DE-2719)1410003},
      pnm          = {342 - Disease Mechanisms and Model Systems (POF3-342)},
      pid          = {G:(DE-HGF)POF3-342},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31919373},
      pmc          = {pmc:PMC6952370},
      doi          = {10.1038/s41467-019-14018-z},
      url          = {https://pub.dzne.de/record/145037},
}