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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao ZENODOarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Data Associated with "Can deep learning models for drug sensitivity prediction truly transfer knowledge from bulk to single cell data?"

Authors: Bohl, Michael; Esteban-Medina, Marina; Lenhof, Kerstin;

Data Associated with "Can deep learning models for drug sensitivity prediction truly transfer knowledge from bulk to single cell data?"

Abstract

This Zenodo record contains all data necessary to reproduce the benchmark results described in the following publication: M. Bohl, M. Esteban-Medina, and K. Lenhof, Can deep learning models for drug sensitivity prediction truly transfer knowledge from bulk to single-cell data? bioRxiv (2025). Processed GDSC and single-cell RNA-Seq datasets are in processed.zip scATD model weights are in checkpoint_fold1_epoch_30.pth Full hyperparameter tuning logs/results are in hyperparameter_tuning_results.csv The first version of the source code (without model weights) is in code.zip. In case it gets updated in the future, check the latest version at https://github.com/cbg-ethz/SC-Bulk-Domain-Adaptation/

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Keywords

RNA-Seq, Domain Adaptation, Cancer

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Related to Research communities
Cancer Research