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 | 3673 | 2019 |
Autoencoding beyond pixels using a learned similarity metric ABL Larsen, SK Sønderby, H Larochelle, O Winther International conference on machine learning, 1558-1566, 2016 | 2538 | 2016 |
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 | 1131 | 2022 |
Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in neural information processing systems 29, 2016 | 1060* | 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 | 1047 | 2017 |
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 | 833 | 2007 |
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 | 722 | 2019 |
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 | 534 | 2019 |
Auxiliary deep generative models L Maaløe, CK Sønderby, SK Sønderby, O Winther International conference on machine learning, 1445-1453, 2016 | 507 | 2016 |
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 | 497 | 2022 |
Sequential neural models with stochastic layers M Fraccaro, SK Sønderby, U Paquet, O Winther Advances in neural information processing systems 29, 2016 | 456 | 2016 |
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line Nature genetics 41 (5), 553-562, 2009 | 419 | 2009 |
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 | 344 | 2017 |
Gaussian processes for classification: Mean-field algorithms M Opper, O Winther Neural computation 12 (11), 2655-2684, 2000 | 323 | 2000 |
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 | 315 | 2016 |
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 | 313 | 2021 |
Expectation consistent approximate inference. M Opper, O Winther, MJ Jordan Journal of Machine Learning Research 6 (12), 2005 | 289 | 2005 |
Bayesian non-negative matrix factorization MN Schmidt, O Winther, LK Hansen Independent Component Analysis and Signal Separation: 8th International …, 2009 | 281 | 2009 |
A Bayesian approach to on-line learning M Opper, O Winther On-line learning in neural networks, 363-378, 1999 | 278 | 1999 |
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 | 262 | 2006 |