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Ole Winther
Ole Winther
Biology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of Denmark
E-mail confirmado em bio.ku.dk - Página inicial
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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
35392019
Autoencoding beyond pixels using a learned similarity metric
ABL Larsen, SK Sønderby, H Larochelle, O Winther
International conference on machine learning, 1558-1566, 2016
24632016
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in neural information processing systems 29, 2016
1025*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
10022017
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
9612022
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
8302007
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
6762019
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
5222019
Auxiliary deep generative models
L Maaløe, CK Sønderby, SK Sønderby, O Winther
International conference on machine learning, 1445-1453, 2016
5032016
Sequential neural models with stochastic layers
M Fraccaro, SK Sønderby, U Paquet, O Winther
Advances in neural information processing systems 29, 2016
4432016
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line
Nature genetics 41 (5), 553-562, 2009
4182009
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
4082022
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
3322017
Gaussian processes for classification: Mean-field algorithms
M Opper, O Winther
Neural computation 12 (11), 2655-2684, 2000
3192000
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
3042016
Expectation consistent approximate inference.
M Opper, O Winther, MJ Jordan
Journal of Machine Learning Research 6 (12), 2005
2832005
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
281*2021
Bayesian non-negative matrix factorization
MN Schmidt, O Winther, LK Hansen
Independent Component Analysis and Signal Separation: 8th International …, 2009
2762009
A Bayesian approach to on-line learning
M Opper, O Winther
On-line learning in neural networks, 363-378, 1999
2751999
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, 1-13, 2006
2622006
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Artigos 1–20