O
Ole Winther
Researcher at Technical University of Denmark
Publications - 289
Citations - 18066
Ole Winther is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Computer science & Bayesian probability. The author has an hindex of 52, co-authored 252 publications receiving 13113 citations. Previous affiliations of Ole Winther include Niels Bohr Institute & University of Copenhagen.
Papers
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Journal ArticleDOI
SignalP 5.0 improves signal peptide predictions using deep neural networks
Jose Juan Almagro Armenteros,Konstantinos D. Tsirigos,Casper Kaae Sønderby,Thomas Nordahl Petersen,Ole Winther,Ole Winther,Søren Brunak,Søren Brunak,Gunnar von Heijne,Gunnar von Heijne,Henrik Nielsen +10 more
TL;DR: A deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs is presented.
Proceedings Article
Autoencoding beyond pixels using a learned similarity metric
TL;DR: In this article, an autoencoder that leverages learned representations to better measure similarities in data space is presented, which can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective.
Journal ArticleDOI
DeepLoc: prediction of protein subcellular localization using deep learning.
Jose Juan Almagro Armenteros,Jose Juan Almagro Armenteros,Casper Kaae Sønderby,Søren Kaae Sønderby,Henrik Nielsen,Ole Winther,Ole Winther +6 more
TL;DR: This work presents a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information, outperforming current state‐of‐the‐art algorithms, including those relying on homology information.
Journal ArticleDOI
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update
Jan Christian Bryne,Eivind Valen,Man-Hung Eric Tang,Troels Marstrand,Ole Winther,Isabelle da Piedade,Anders Krogh,Boris Lenhard,Albin Sandelin +8 more
TL;DR: JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns that has been expanded and equipped with additional functions aimed at both casual and power users.
Journal ArticleDOI
SignalP 6.0 predicts all five types of signal peptides using protein language models
Felix Teufel,Jose Juan Almagro Armenteros,Alexander Rosenberg Johansen,Magnús Halldór Gíslason,Silas Irby Pihl,Konstantinos D. Tsirigos,Ole Winther,Søren Brunak,Gunnar von Heijne,Henrik Nielsen +9 more
TL;DR: Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms as mentioned in this paper , and they can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs.