%0 Generic %1 Hryhorii2021 %A Chereda, Hryhorii %A Bleckmann, A. %A Menck, Kerstin %A Perera-Bel, Júlia %A Stegmaier, P. %A Auer, Florian %A Kramer, F. %A Leha, A. %A Beissbarth, T. %D 2021 %J Genome Medicine %K deep_learning healthcare interpretability precision_medicine breast_cancer posted_with_chatgpt %P 1-16 %R 10.1186/s13073-021-00845-7 %T Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer %U https://www.semanticscholar.org/paper/df73fca81d247a64075d893f256ec8dbf084078f %V 13

@JournalArticle{Hryhorii2021, added-at = {2023-11-30T18:11:38.000+0100}, author = {Chereda, Hryhorii and Bleckmann, A. and Menck, Kerstin and Perera-Bel, Júlia and Stegmaier, P. and Auer, Florian and Kramer, F. and Leha, A. and Beissbarth, T.}, biburl = {https://www.bibsonomy.org/bibtex/252c110af82c3f1efc8612557ec09f5b2/elnaz}, day = 11, description = {This paper discusses the application of deep learning in healthcare, focusing on interpretability methods for explaining patient-specific decisions of deep learning techniques. It emphasizes the importance of these methods in personalized precision medicine, particularly for metastasis prediction in breast cancer.}, doi = {10.1186/s13073-021-00845-7}, interhash = {0b8a4f0239a9d4df76e7bc7ad6cca35b}, intrahash = {52c110af82c3f1efc8612557ec09f5b2}, journal = {Genome Medicine}, keywords = {deep_learning healthcare interpretability precision_medicine breast_cancer posted_with_chatgpt}, month = {3}, pages = {1-16}, timestamp = {2023-11-30T18:11:38.000+0100}, title = {Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer}, url = {https://www.semanticscholar.org/paper/df73fca81d247a64075d893f256ec8dbf084078f}, volume = 13, year = 2021 }