TY  - JOUR
AU  - Jiao, Wei
AU  - Atwal, Gurnit
AU  - Polak, Paz
AU  - Karlic, Rosa
AU  - Cuppen, Edwin
AU  - PCAWGTumorSubtypesClinicalTranslationWorkingGroup
AU  - Danyi, Alexandra
AU  - de Ridder, Jeroen
AU  - van Herpen, Carla
AU  - Lolkema, Martijn P
AU  - Steeghs, Neeltje
AU  - Getz, Gad
AU  - Morris, Quaid
AU  - Stein, Lincoln D
AU  - PCAWGConsortium
TI  - A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.
JO  - Nature Communications
VL  - 11
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Nature Publishing Group UK
M1  - DKFZ-2021-02572
SP  - 728
PY  - 2020
N1  - siehe Correction: DKFZ Autoren affiliiert im PCAWG Consortium: https://inrepo02.dkfz.de/record/212437   /  https://doi.org/10.1038/s41467-022-32329-6
AB  - In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3
KW  - Computational Biology: methods
KW  - Deep Learning
KW  - Female
KW  - Genome, Human
KW  - Humans
KW  - Male
KW  - Mutation
KW  - Neoplasm Metastasis
KW  - Neoplasms: genetics
KW  - Neoplasms: pathology
KW  - Reproducibility of Results
KW  - Whole Genome Sequencing
LB  - PUB:(DE-HGF)16
C6  - pmid:32024849
C2  - pmc:PMC7002586
DO  - DOI:10.1038/s41467-019-13825-8
UR  - https://inrepo02.dkfz.de/record/177485
ER  -