| 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 | 4570 | 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 | 3079 | 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 | 2711 | 2022 |
| 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 | 1364 | 2022 |
| DeepLoc: prediction of protein subcellular localization using deep learning JJ Almagro Armenteros, CK Sønderby, SK Sønderby, H Nielsen, ... Bioinformatics 33 (21), 3387-3395, 2017 | 1309 | 2017 |
| Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in neural information processing systems 29, 2016 | 1303* | 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 | 861 | 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 | 793 | 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 | 698 | 2019 |
| DeepLoc 2.0: multi-label subcellular localization prediction using protein language models V Thumuluri, JJ Almagro Armenteros, AR Johansen, H Nielsen, O Winther Nucleic acids research 50 (W1), W228-W234, 2022 | 651 | 2022 |
| 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 | 606 | 2021 |
| Detecting novel sequence signals in targeting peptides using deep learning JJ Almagro Armenteros, M Salvatore, O Emanuelsson, O Winther, ... bioRxiv, 639203, 2019 | 587 | 2019 |
| Auxiliary deep generative models L Maaløe, CK Sønderby, SK Sønderby, O Winther International conference on machine learning, 1445-1453, 2016 | 555 | 2016 |
| Sequential neural models with stochastic layers M Fraccaro, SK Sønderby, U Paquet, O Winther Advances in neural information processing systems 29, 2016 | 527 | 2016 |
| Can large language models reason about medical questions? V Liévin, CE Hother, AG Motzfeldt, O Winther Patterns 5 (3), 2024 | 473 | 2024 |
| The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line Nature genetics 41 (5), 553-562, 2009 | 439 | 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 | 401 | 2017 |
| 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 | 362 | 2016 |
| Gaussian processes for classification: Mean-field algorithms M Opper, O Winther Neural computation 12 (11), 2655-2684, 2000 | 342 | 2000 |
| Expectation consistent approximate inference. M Opper, O Winther, MJ Jordan Journal of Machine Learning Research 6 (12), 2005 | 316 | 2005 |
Casper Kaae SønderbyFounder@twig.energy, ML/DL Research ScientistVerified email at twig.energy
