Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Viktoria Schuster"'
Autor:
Iñigo Prada-Luengo, Viktoria Schuster, Yuhu Liang, Thilde Terkelsen, Valentina Sora, Anders Krogh
Publikováno v:
Genome Biology, Vol 24, Iss 1, Pp 1-17 (2023)
Abstract Differential analysis of bulk RNA-seq data often suffers from lack of good controls. Here, we present a generative model that replaces controls, trained solely on healthy tissues. The unsupervised model learns a low-dimensional representatio
Externí odkaz:
https://doaj.org/article/a01c7581a83340df990a066ff0394ff0
Autor:
Alessandro Montemurro, Viktoria Schuster, Helle Rus Povlsen, Amalie Kai Bentzen, Vanessa Jurtz, William D. Chronister, Austin Crinklaw, Sine R. Hadrup, Ole Winther, Bjoern Peters, Leon Eyrich Jessen, Morten Nielsen
Publikováno v:
Communications Biology, Vol 4, Iss 1, Pp 1-13 (2021)
Montemurro et al. present NetTCR-2.0, a convolutional neural network-based tool for predicting the interactions between T cell receptors and MHC-peptide complexes. This tool demonstrates that the best predictions are made when CDR3 α or CDR3 β bind
Externí odkaz:
https://doaj.org/article/0fd24268ab1c4d9e95f4d96aaa1b710a
Autor:
Viktoria Schuster, Anders Krogh
Publikováno v:
Entropy, Vol 23, Iss 11, p 1403 (2021)
Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder i
Externí odkaz:
https://doaj.org/article/d20c216f39bc4836996f2afcc9ba9edc
Autor:
Anders Krogh, Viktoria Schuster
Publikováno v:
Entropy
Volume 23
Issue 11
Entropy, Vol 23, Iss 1403, p 1403 (2021)
Schuster, V & Krogh, A 2021, ' A manifold learning perspective on representation learning : Learning decoder and representations without an encoder ', Entropy, vol. 23, no. 11, 1403 . https://doi.org/10.3390/e23111403
Volume 23
Issue 11
Entropy, Vol 23, Iss 1403, p 1403 (2021)
Schuster, V & Krogh, A 2021, ' A manifold learning perspective on representation learning : Learning decoder and representations without an encoder ', Entropy, vol. 23, no. 11, 1403 . https://doi.org/10.3390/e23111403
Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder i
Autor:
Amalie Kai Bentzen, William D. Chronister, Morten Nielsen, Helle Rus Povlsen, Viktoria Schuster, Ole Winther, Vanessa Isabell Jurtz, Sine Reker Hadrup, Austin Crinklaw, Bjoern Peters, Alessandro Montemurro, Leon Eyrich Jessen
Publikováno v:
Montemurro, A, Schuster, V, Povlsen, H R, Bentzen, A K, Jurtz, V, Chronister, W D, Crinklaw, A, Hadrup, S R, Winther, O, Peters, B, Jessen, L E & Nielsen, M 2021, ' NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data ', Communications Biology, vol. 4, no. 1, 1060 . https://doi.org/10.1038/s42003-021-02610-3
Communications Biology, Vol 4, Iss 1, Pp 1-13 (2021)
Montemurro, A, Schuster, V, Povlsen, H R, Bentzen, A K, Jurtz, V, Chronister, W D, Crinklaw, A, Hadrup, S R, Winther, O, Peters, B, Jessen, L E & Nielsen, M 2021, ' NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data ', Communications Biology, vol. 4, 1060 . https://doi.org/10.1038/s42003-021-02610-3
Communications Biology, Vol 4, Iss 1, Pp 1-13 (2021)
Montemurro, A, Schuster, V, Povlsen, H R, Bentzen, A K, Jurtz, V, Chronister, W D, Crinklaw, A, Hadrup, S R, Winther, O, Peters, B, Jessen, L E & Nielsen, M 2021, ' NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data ', Communications Biology, vol. 4, 1060 . https://doi.org/10.1038/s42003-021-02610-3
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that “shall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f52488649735adc2c45a109b91698e86
https://orbit.dtu.dk/en/publications/54a18255-04b9-43e5-99d1-81af602e0d78
https://orbit.dtu.dk/en/publications/54a18255-04b9-43e5-99d1-81af602e0d78