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Ole Winther
Ole Winther
Biology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of Denmark
Email verificata su bio.ku.dk - Home page
Titolo
Citata da
Citata da
Anno
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
35612019
Autoencoding beyond pixels using a learned similarity metric
ABL Larsen, SK Sønderby, H Larochelle, O Winther
International conference on machine learning, 1558-1566, 2016
24762016
Ladder variational autoencoders
CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther
Advances in neural information processing systems 29, 2016
1031*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
10092017
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
9842022
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
8312007
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
6852019
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
5242019
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
4452016
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
4252022
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line
Nature genetics 41 (5), 553-562, 2009
4182009
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
3352017
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
3072016
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
288*2021
Expectation consistent approximate inference.
M Opper, O Winther, MJ Jordan
Journal of Machine Learning Research 6 (12), 2005
2852005
Bayesian non-negative matrix factorization
MN Schmidt, O Winther, LK Hansen
Independent Component Analysis and Signal Separation: 8th International …, 2009
2772009
A Bayesian approach to on-line learning
M Opper, O Winther
On-line learning in neural networks, 363-378, 1999
2761999
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
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
Articoli 1–20