Ole Winther

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Casper Kaae SønderbyFounder@twig.energy, ML/DL Research ScientistVerified email at twig.energy
Søren Kaae SønderbyVerified email at bio.ku.dk
José Juan Almagro ArmenterosPostdoc, Stanford UniversityVerified email at stanford.edu
Lars Kai HansenProfessor, Cognitive Systems, DTU Compute, Technical University of DenmarkVerified email at dtu.dk
Henrik NielsenAssociate Professor, Department of Health Technology, Technical University of DenmarkVerified email at dtu.dk
Lars MaaløeCo-Founder & CTO @ Corti | Adj. Assoc. Professor of Machine Learning @ DTUVerified email at corti.ai
Frederik Otzen BaggerUniversity of Cambridge and EMBL-EBIVerified email at ebi.ac.uk
Gunnar von HeijneStockholm UniversityVerified email at dbb.su.se
Søren BrunakProfessor of Disease Systems Biology, University of CopenhagenVerified email at cpr.ku.dk
Anders KroghDepartment of Computer Science and Center for Health Data Science, University of CopenhagenVerified email at di.ku.dk
Konstantinos Tsirigos, PhD Bioinforma...Cognizant DenmarkVerified email at cognizant.com
Alexander Rosenberg JohansenStanford UniversityVerified email at stanford.edu
Marco FraccaroTechnical University of DenmarkVerified email at dtu.dk
Nicolas RapindenmarkVerified email at ngc.dk
Ricardo HenaoKing Abdullah University of Science and Technology, Duke UniversityVerified email at duke.edu
Thomas Nordahl PetersenDTU Food, National Food Institute, DenmarkVerified email at food.dtu.dk
Albin SandelinThe Bioinformatics Centre, Department of Biology & BRIC, Copenhagen UniversityVerified email at binf.ku.dk
Jonas BuskTechnical University of DenmarkVerified email at dtu.dk
Finn Cilius NIelsenProfessorVerified email at regionh.dk
Maria Faurholt-JepsenPsychiatric Center Copenhagen, DenmarkVerified email at faurholt-jepsen.dk

Ole Winther

Biology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University of Denmark

Verified email at bio.ku.dk - Homepage

machine learning bioinformatics deep learning artificial intelligence


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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	2922	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	2126	2016
Ladder variational autoencoders CK Sønderby, T Raiko, L Maaløe, SK Sønderby, O Winther Advances in neural information processing systems 29, 2016	888*	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	832	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	806	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	475	2019
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line Nature genetics 41 (5), 553-562, 2009	473	2009
Auxiliary deep generative models L Maaløe, CK Sønderby, SK Sønderby, O Winther International conference on machine learning, 1445-1453, 2016	466	2016
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	438	2019
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	413	2022
Sequential neural models with stochastic layers M Fraccaro, SK Sønderby, U Paquet, O Winther Advances in neural information processing systems 29, 2016	386	2016
Gaussian processes for classification: Mean-field algorithms M Opper, O Winther Neural computation 12 (11), 2655-2684, 2000	313	2000
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	286	2017
Expectation consistent approximate inference. M Opper, O Winther, MJ Jordan Journal of Machine Learning Research 6 (12), 2005	273	2005
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	269	2016
Bayesian non-negative matrix factorization MN Schmidt, O Winther, LK Hansen Independent Component Analysis and Signal Separation: 8th International …, 2009	264	2009
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	253	2006
A Bayesian approach to on-line learning M Opper, O Winther Cambridge University Press, 1999	253	1999
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	226	2009
Mean-field approaches to independent component analysis PAFR Højen-Sørensen, O Winther, LK Hansen Neural Computation 14 (4), 889-918, 2002	212	2002

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