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@ARTICLE{Yuan:301911,
      author       = {D. Yuan and R. Jugas and P. Pokorna and J. Sterba and O.
                      Slaby and S. Schmid and C. Siewert and B. Osberg and D.
                      Capper$^*$ and S. Halldorsson and E. O. Vik-Mo and P. S.
                      Zeiner and K. Weber$^*$ and P. N. Harter and C. Thomas and
                      A. Albers and M. Rechsteiner and R. Reimann and A. Appelt
                      and U. Schüller and N. Jabareen and S. Mackowiak and N.
                      Ishaque and R. Eils and S. Lukassen and P. Euskirchen$^*$},
      title        = {cross{NN} is an explainable framework for cross-platform
                      {DNA} methylation-based classification of tumors.},
      journal      = {Nature cancer},
      volume       = {6},
      number       = {7},
      issn         = {2662-1347},
      address      = {London},
      publisher    = {Nature Research},
      reportid     = {DKFZ-2025-01181},
      pages        = {1283-1294},
      year         = {2025},
      note         = {2025 Jul;6(7):1283-1294},
      abstract     = {DNA methylation-based classification of (brain) tumors has
                      emerged as a powerful and indispensable diagnostic
                      technique. Initial implementations used methylation
                      microarrays for data generation, while most current
                      classifiers rely on a fixed methylation feature space. This
                      makes them incompatible with other platforms, especially
                      different flavors of DNA sequencing. Here, we describe
                      crossNN, a neural network-based machine learning framework
                      that can accurately classify tumors using sparse methylomes
                      obtained on different platforms and with different epigenome
                      coverage and sequencing depth. It outperforms other deep and
                      conventional machine learning models regarding accuracy and
                      computational requirements while still being explainable. We
                      use crossNN to train a pan-cancer classifier that can
                      discriminate more than 170 tumor types across all organ
                      sites. Validation in more than 5,000 tumors profiled on
                      different platforms, including nanopore and targeted
                      bisulfite sequencing, demonstrates its robustness and
                      scalability with $99.1\%$ and $97.8\%$ precision for the
                      brain tumor and pan-cancer models, respectively.},
      cin          = {BE01 / FM01},
      ddc          = {610},
      cid          = {I:(DE-He78)BE01-20160331 / I:(DE-He78)FM01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40481322},
      doi          = {10.1038/s43018-025-00976-5},
      url          = {https://inrepo02.dkfz.de/record/301911},
}