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Viktoria Schuster

Showing results (1-10 of 5) with videos related to

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Bioinformatics (Oxford, England)|August 12, 2023
The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA dataViktoria Schuster, Anders Krogh
Entropy (Basel, Switzerland)|November 27, 2021
A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an EncoderViktoria Schuster, Anders Krogh
Nature Communications|November 20, 2024
multiDGD: A versatile deep generative model for multi-omics dataViktoria Schuster, Emma Dann, Anders Krogh, et al.
Genome Biology|November 17, 2023
N-of-one differential gene expression without control samples using a deep generative modelIñigo Prada-Luengo, Viktoria Schuster, Yuhu Liang, et al.
Communications Biology|September 11, 2021
NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence dataAlessandro Montemurro, Viktoria Schuster, Helle Rus Povlsen, et al.
Pageof 1

Showing results (1-10 of 5) with videos related to

Sort By:
Pageof 1
Bioinformatics (Oxford, England)|August 12, 2023
The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA dataViktoria Schuster, Anders Krogh
Entropy (Basel, Switzerland)|November 27, 2021
A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an EncoderViktoria Schuster, Anders Krogh
Nature Communications|November 20, 2024
multiDGD: A versatile deep generative model for multi-omics dataViktoria Schuster, Emma Dann, Anders Krogh, et al.
Genome Biology|November 17, 2023
N-of-one differential gene expression without control samples using a deep generative modelIñigo Prada-Luengo, Viktoria Schuster, Yuhu Liang, et al.
Communications Biology|September 11, 2021
NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence dataAlessandro Montemurro, Viktoria Schuster, Helle Rus Povlsen, et al.
Pageof 1