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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
25402019
Autoencoding beyond pixels using a learned similarity metric
ABL Larsen, SK Sønderby, H Larochelle, O Winther
International conference on machine learning, 1558-1566, 2016
18922016
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in neural information processing systems 29, 2016
812*2016
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
7992007
DeepLoc: prediction of protein subcellular localization using deep learning
JJ Almagro Armenteros, CK Sønderby, SK Sønderby, H Nielsen, ...
Bioinformatics 33 (21), 3387-3395, 2017
7352017
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line
Nature genetics 41 (5), 553-562, 2009
4672009
Auxiliary deep generative models
L Maaløe, CK Sønderby, SK Sønderby, O Winther
International conference on machine learning, 1445-1453, 2016
4442016
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
3762019
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
3732019
Sequential neural models with stochastic layers
M Fraccaro, SK Sønderby, U Paquet, O Winther
Advances in neural information processing systems 29, 2016
3562016
Gaussian processes for classification: Mean-field algorithms
M Opper, O Winther
Neural computation 12 (11), 2655-2684, 2000
3162000
Expectation consistent approximate inference.
M Opper, O Winther, MJ Jordan
Journal of Machine Learning Research 6 (12), 2005
2682005
Bayesian non-negative matrix factorization
MN Schmidt, O Winther, LK Hansen
International Conference on Independent Component Analysis and Signal …, 2009
2602009
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
2592017
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
2562016
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
2462006
A Bayesian approach to on-line learning
M Opper, O Winther
Cambridge University Press, 1999
2411999
RSK is a principal effector of the RAS-ERK pathway for eliciting a coordinate promotile/invasive gene program and phenotype in epithelial cells
U Doehn, C Hauge, SR Frank, CJ Jensen, K Duda, JV Nielsen, ...
Molecular cell 35 (4), 511-522, 2009
2162009
Mean-field approaches to independent component analysis
PAFR Højen-Sørensen, O Winther, LK Hansen
Neural Computation 14 (4), 889-918, 2002
2102002
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, 1-3, 2022
1802022
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