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@ARTICLE{Menden:153282,
      author       = {Menden, Kevin and Marouf, Mohamed and Oller, Sergio and
                      Dalmia, Anupriya and Magruder, Daniel Sumner and Kloiber,
                      Karin and Heutink, Peter and Bonn, Stefan},
      title        = {{D}eep learning–based cell composition analysis from
                      tissue expression profiles},
      journal      = {Science advances},
      volume       = {6},
      number       = {30},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {DZNE-2020-01279},
      pages        = {eaba2619 -},
      year         = {2020},
      abstract     = {We present Scaden, a deep neural network for cell
                      deconvolution that uses gene expression information to infer
                      the cellular composition of tissues. Scaden is trained on
                      single-cell RNA sequencing (RNA-seq) data to engineer
                      discriminative features that confer robustness to bias and
                      noise, making complex data preprocessing and feature
                      selection unnecessary. We demonstrate that Scaden
                      outperforms existing deconvolution algorithms in both
                      precision and robustness. A single trained network reliably
                      deconvolves bulk RNA-seq and microarray, human and mouse
                      tissue expression data and leverages the combined
                      information of multiple datasets. Because of this stability
                      and flexibility, we surmise that deep learning will become
                      an algorithmic mainstay for cell deconvolution of various
                      data types. Scaden’s software package and web application
                      are easy to use on new as well as diverse existing
                      expression datasets available in public resources, deepening
                      the molecular and cellular understanding of developmental
                      and disease processes.},
      cin          = {AG Bonn 1 / AG Heutink},
      ddc          = {500},
      cid          = {I:(DE-2719)1410003 / I:(DE-2719)1210002},
      pnm          = {342 - Disease Mechanisms and Model Systems (POF3-342) / 345
                      - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-342 / G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32832661},
      pmc          = {pmc:PMC7439569},
      doi          = {10.1126/sciadv.aba2619},
      url          = {https://pub.dzne.de/record/153282},
}