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Ole Winther
Ole Winther
Biology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of Denmark
Verified email at bio.ku.dk - Homepage
Title
Cited by
Cited by
Year
SignalP 5.0 improves signal peptide predictions using deep neural networks
JJ Almagro Armenteros, KD Tsirigos, CK Sønderby, TN Petersen, ...
Nature biotechnology 37 (4), 420-423, 2019
34622019
Autoencoding beyond pixels using a learned similarity metric
ABL Larsen, SK Sønderby, H Larochelle, O Winther
International conference on machine learning, 1558-1566, 2016
24222016
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in neural information processing systems 29, 2016
1005*2016
DeepLoc: prediction of protein subcellular localization using deep learning
JJ Almagro Armenteros, CK Sønderby, SK Sønderby, H Nielsen, ...
Bioinformatics 33 (21), 3387-3395, 2017
9852017
SignalP 6.0 predicts all five types of signal peptides using protein language models
F Teufel, JJ Almagro Armenteros, AR Johansen, MH Gíslason, SI Pihl, ...
Nature biotechnology 40 (7), 1023-1025, 2022
8782022
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update
JC Bryne, E Valen, MHE Tang, T Marstrand, O Winther, I da Piedade, ...
Nucleic acids research 36 (suppl_1), D102-D106, 2007
8252007
Detecting sequence signals in targeting peptides using deep learning
JJA Armenteros, M Salvatore, O Emanuelsson, O Winther, G Von Heijne, ...
Life science alliance 2 (5), 2019
6452019
NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning
MS Klausen, MC Jespersen, H Nielsen, KK Jensen, VI Jurtz, ...
Proteins: Structure, Function, and Bioinformatics 87 (6), 520-527, 2019
5102019
Auxiliary deep generative models
L Maaløe, CK Sønderby, SK Sønderby, O Winther
International conference on machine learning, 1445-1453, 2016
5042016
Sequential neural models with stochastic layers
M Fraccaro, SK Sønderby, U Paquet, O Winther
Advances in neural information processing systems 29, 2016
4362016
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line
Nature genetics 41 (5), 553-562, 2009
4162009
DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks
J Hallgren, KD Tsirigos, MD Pedersen, JJ Almagro Armenteros, ...
BioRxiv, 2022.04. 08.487609, 2022
3582022
A disentangled recognition and nonlinear dynamics model for unsupervised learning
M Fraccaro, S Kamronn, U Paquet, O Winther
Advances in neural information processing systems 30, 2017
3232017
Gaussian processes for classification: Mean-field algorithms
M Opper, O Winther
Neural computation 12 (11), 2655-2684, 2000
3162000
BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis
FO Bagger, D Sasivarevic, SH Sohi, LG Laursen, S Pundhir, CK Sønderby, ...
Nucleic acids research 44 (D1), D917-D924, 2016
2982016
Expectation consistent approximate inference.
M Opper, O Winther, MJ Jordan
Journal of Machine Learning Research 6 (12), 2005
2832005
Bayesian non-negative matrix factorization
MN Schmidt, O Winther, LK Hansen
Independent Component Analysis and Signal Separation: 8th International …, 2009
2762009
Improved metagenome binning and assembly using deep variational autoencoders
JN Nissen, J Johansen, RL Allesøe, CK Sønderby, JJA Armenteros, ...
Nature biotechnology 39 (5), 555-560, 2021
269*2021
A Bayesian approach to on-line learning
M Opper, O Winther
Cambridge University Press, 1999
2671999
Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae
B Regenberg, T Grotkjær, O Winther, A Fausbøll, M Åkesson, C Bro, ...
Genome biology 7 (11), 1-13, 2006
2592006
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