Explaining decisions of graph convolutional neural networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer
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%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
}