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@ARTICLE{Keyl:186729,
      author       = {P. Keyl and P. Bischoff$^*$ and G. Dernbach and M. Bockmayr
                      and R. Fritz and D. Horst$^*$ and N. Blüthgen and G.
                      Montavon and K.-R. Müller and F. Klauschen$^*$},
      title        = {{S}ingle-cell gene regulatory network prediction by
                      explainable {AI}.},
      journal      = {Nucleic acids research},
      volume       = {51},
      number       = {4},
      issn         = {0305-1048},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2023-00081},
      pages        = {e20},
      year         = {2023},
      note         = {2023 Feb 28;51(4):e20},
      abstract     = {The molecular heterogeneity of cancer cells contributes to
                      the often partial response to targeted therapies and relapse
                      of disease due to the escape of resistant cell populations.
                      While single-cell sequencing has started to improve our
                      understanding of this heterogeneity, it offers a mostly
                      descriptive view on cellular types and states. To obtain
                      more functional insights, we propose scGeneRAI, an
                      explainable deep learning approach that uses layer-wise
                      relevance propagation (LRP) to infer gene regulatory
                      networks from static single-cell RNA sequencing data for
                      individual cells. We benchmark our method with synthetic
                      data and apply it to single-cell RNA sequencing data of a
                      cohort of human lung cancers. From the predicted single-cell
                      networks our approach reveals characteristic network
                      patterns for tumor cells and normal epithelial cells and
                      identifies subnetworks that are observed only in (subgroups
                      of) tumor cells of certain patients. While current
                      state-of-the-art methods are limited by their ability to
                      only predict average networks for cell populations, our
                      approach facilitates the reconstruction of networks down to
                      the level of single cells which can be utilized to
                      characterize the heterogeneity of gene regulation within and
                      across tumors.},
      cin          = {BE01 / MU01},
      ddc          = {570},
      cid          = {I:(DE-He78)BE01-20160331 / I:(DE-He78)MU01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36629274},
      doi          = {10.1093/nar/gkac1212},
      url          = {https://inrepo02.dkfz.de/record/186729},
}