A queue-poll extension and DataSHIELD: standardised, monitored, Indirect and secure access to sensitive data
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
ICT for health science research : proceedings of the EFMI 2019 Special Topic Conference. - Amsterdam, 2019. - 115-119, ISBN: 978-1-61499-959-1
- Event
-
Veröffentlichung
- (where)
-
Freiburg
- (who)
-
Universität
- (when)
-
2020
- Creator
- DOI
-
10.3233/978-1-61499-959-1-115
- URN
-
urn:nbn:de:bsz:25-freidok-1518666
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
26.03.2025, 9:03 AM 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
- Gruendner, Julian
- Prokosch, Hans-Ulrich
- Schindler, Sebastian
- Lenz, Stefan
- Binder, Harald
- Universität
Time of origin
- 2020
Other Objects (12)
Deep generative models in DataSHIELD
Flexible semi- and non-parametric modelling and prognosis for discrete outcomes
Exploring generative deep learning for omics data using log-linear models
Interpretable generative deep learning: an illustration with single cell gene expression data
Modeling activity tracker data using deep Boltzmann machines
Deep learning on biomedical data with Boltzmann machines
Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: applications to distributed computing under disclosure constraints
pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components
Evolution : ein kritisches Lehrbuch
Sequential learning of regression models by penalized estimation
Needs assessment towards research data management at the Medical Faculty of the University of Freiburg – Data of the BE-KONFORM study
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Deep generative models in DataSHIELD
Flexible semi- and non-parametric modelling and prognosis for discrete outcomes
Exploring generative deep learning for omics data using log-linear models
Interpretable generative deep learning: an illustration with single cell gene expression data
Modeling activity tracker data using deep Boltzmann machines
Deep learning on biomedical data with Boltzmann machines
Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: applications to distributed computing under disclosure constraints
pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components
Evolution : ein kritisches Lehrbuch
Sequential learning of regression models by penalized estimation
Needs assessment towards research data management at the Medical Faculty of the University of Freiburg – Data of the BE-KONFORM study
The performance of deep generative models for learning joint embeddings of single-cell multi-omics data
Deep generative models in DataSHIELD
Flexible semi- and non-parametric modelling and prognosis for discrete outcomes
Exploring generative deep learning for omics data using log-linear models
Interpretable generative deep learning: an illustration with single cell gene expression data
Modeling activity tracker data using deep Boltzmann machines
Deep learning on biomedical data with Boltzmann machines
Recovery of original individual person data (IPD) inferences from empirical IPD summaries only: applications to distributed computing under disclosure constraints
pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components
Evolution : ein kritisches Lehrbuch
Sequential learning of regression models by penalized estimation
Needs assessment towards research data management at the Medical Faculty of the University of Freiburg – Data of the BE-KONFORM study