Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Hryhorii Chereda"'
Autor:
Hryhorii Chereda, Annalen Bleckmann, Kerstin Menck, Júlia Perera-Bel, Philip Stegmaier, Florian Auer, Frank Kramer, Andreas Leha, Tim Beißbarth
Publikováno v:
Genome Medicine, Vol 13, Iss 1, Pp 1-16 (2021)
Abstract Background Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often consid
Externí odkaz:
https://doaj.org/article/f1afd85784f749c9a222fea3c981750d
Publikováno v:
PLoS ONE, Vol 16, Iss 10 (2021)
Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been
Externí odkaz:
https://doaj.org/article/9efd9bc828c7462f878a3427d7391f02
Autor:
Bastian Pfeifer, Hryhorii Chereda, Roman Martin, Anna Saranti, Sandra Clemens, Anne-Christin Hauschild, Tim Beißbarth, Andreas Holzinger, Dominik Heider
Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad429bfdb08697c3a06ba648f3ae3841
https://doi.org/10.1101/2023.03.22.533772
https://doi.org/10.1101/2023.03.22.533772
MotivationColorectal Cancer has the second-highest mortality rate worldwide, which requires advanced diagnostics and individualized therapies to be developed. Information about the interactions between molecular entities provides valuable information
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ce7caad9ea52500c3e295a94d8daea0
https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/104810
https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/104810
High-throughput technologies are increasingly important in discovering prognostic molecular signatures and identifying novel drug targets. Molecular signatures can be obtained as a subset of features that are important for the decisions of a Machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd10d04f5632aee345ebb27c20718dc6
https://doi.org/10.1101/2021.12.26.474194
https://doi.org/10.1101/2021.12.26.474194
Autor:
Philip Stegmaier, Júlia Perera-Bel, Annalen Bleckmann, Florian Auer, Kerstin Menck, Hryhorii Chereda, Frank Kramer, Tim Beißbarth, Andreas Leha
Publikováno v:
Genome Medicine
Genome Medicine, Vol 13, Iss 1, Pp 1-16 (2021)
Genome Medicine, Vol 13, Iss 1, Pp 1-16 (2021)
MotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::319e30ee649c3f00fb4aa67bdd495f7f
http://hdl.handle.net/10230/47911
http://hdl.handle.net/10230/47911
Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6856d49d08277fce569ad6acc324a854
https://doi.org/10.1101/2020.12.09.417733
https://doi.org/10.1101/2020.12.09.417733