%0 Journal Article
%A Jiao, Wei
%A Atwal, Gurnit
%A Polak, Paz
%A Karlic, Rosa
%A Cuppen, Edwin
%A PCAWGTumorSubtypesClinicalTranslationWorkingGroup
%A Danyi, Alexandra
%A de Ridder, Jeroen
%A van Herpen, Carla
%A Lolkema, Martijn P
%A Steeghs, Neeltje
%A Getz, Gad
%A Morris, Quaid
%A Stein, Lincoln D
%A PCAWGConsortium
%T A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.
%J Nature Communications
%V 11
%N 1
%@ 2041-1723
%C [London]
%I Nature Publishing Group UK
%M DKFZ-2021-02572
%P 728
%D 2020
%Z siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212437   /  https://doi.org/10.1038/s41467-022-32329-6
%X In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3
%K Computational Biology: methods
%K Deep Learning
%K Female
%K Genome, Human
%K Humans
%K Male
%K Mutation
%K Neoplasm Metastasis
%K Neoplasms: genetics
%K Neoplasms: pathology
%K Reproducibility of Results
%K Whole Genome Sequencing
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:32024849
%2 pmc:PMC7002586
%R 10.1038/s41467-019-13825-8
%U https://inrepo02.dkfz.de/record/177485