Detection of Patients with Congenital and Often Concealed Long-QT Syndrome by Novel Deep Learning Models
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
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In: Journal of Personalized Medicine 12 (2022) 7, 1135, 1-9
- Event
-
Veröffentlichung
- (where)
-
Münster
- (who)
-
Universitäts- und Landesbibliothek Münster
- (when)
-
2023
- Creator
-
Doldi, Florian
Plagwitz, Lucas
Hoffmann, Lea Philine
Rath, Benjamin
Frommeyer, Gerrit
Reinke, Florian Johann
Leitz, Patrick R.
Büscher, Antonius
Güner, Fatih
Brix, Tobias
Wegner, Felix K.
Willy, Kevin Kurt
Hanel, Yvonne
Dittmann, Sven
Haverkamp, Wilhelm
Schulze-Bahr, Eric
Varghese, Julian
Eckardt, Lars
- DOI
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10.17879/10039605644
- URN
-
urn:nbn:de:hbz:6-10039558962
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:47 PM CET
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Doldi, Florian
- Plagwitz, Lucas
- Hoffmann, Lea Philine
- Rath, Benjamin
- Frommeyer, Gerrit
- Reinke, Florian Johann
- Leitz, Patrick R.
- Büscher, Antonius
- Güner, Fatih
- Brix, Tobias
- Wegner, Felix K.
- Willy, Kevin Kurt
- Hanel, Yvonne
- Dittmann, Sven
- Haverkamp, Wilhelm
- Schulze-Bahr, Eric
- Varghese, Julian
- Eckardt, Lars
- Universitäts- und Landesbibliothek Münster
Time of origin
- 2023
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Modeling short QT syndrome using human‐induced pluripotent stem cell–derived cardiomyocytes
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Clinical utility gene card for: Long-QT syndrome
Often Graceful
So Often Sad
Molecular mechanism of autosomal eecessive long QT-syndrome 1 without deafness
Modeling short QT syndrome using human‐induced pluripotent stem cell–derived cardiomyocytes
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