2026 Ole Winther: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	57	3735	3630	13	13	225	23715

Ole Winther publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Ole Winther sits on this spectrum.

32 publications 991+

This scientist: 225 publications — 55th percentile

55% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Ole Winther D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Ole Winther sits on this spectrum.

30 D-Index 131+

This scientist: 57 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

2025 - Research.com Computer Science in Denmark Leader Award
2022 - Research.com Computer Science in Denmark Leader Award

Overview

Ole Winther is affiliated with the Technical University of Denmark and has contributed extensively to research at the intersection of biochemistry, genetics, molecular biology, and computer science. Their work spans multiple subfields, including molecular biology, artificial intelligence, computer vision and pattern recognition, materials chemistry, and genetics.

The scientist's research encompasses several key topics:

Machine Learning in Bioinformatics
RNA and protein synthesis mechanisms
Machine Learning in Materials Science
Genomics and Phylogenetic Studies
Vaccines and immunoinformatics approaches
Topic Modeling
Protein Structure and Dynamics

Ole Winther has published in a variety of venues, with frequent contributions to:

arXiv (Cornell University)
bioRxiv (Cold Spring Harbor Laboratory)
Zenodo (CERN European Organization for Nuclear Research)
Nucleic Acids Research
Bioinformatics

Frequent coauthors collaborating with Ole Winther include:

Valentin Liévin
Frederik Otzen Bagger
Felix Teufel
Henrik Nielsen
Christoffer Hother

Among recent publications, some notable works are:

"SignalP 6.0 predicts all five types of signal peptides using protein language models," 2022, Repository for Publications and Research Data (ETH Zurich)
"DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks," 2022, bioRxiv (Cold Spring Harbor Laboratory)
"DeepLoc 2.0: multi-label subcellular localization prediction using protein language models," 2022, Nucleic Acids Research
"Improved metagenome binning and assembly using deep variational autoencoders," 2021, Nature Biotechnology
"NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning," 2022, Nucleic Acids Research

Ole Winther's research integrates advanced computational methods such as deep learning and neural networks with biological data, aiming to enhance understanding and predictive capabilities in protein biology and genomics. Their work on protein language models contributes to analyses of signal peptides, transmembrane proteins, and subcellular localization, addressing challenges in protein structure and dynamics.

The scientist's approach demonstrates interdisciplinary application of machine learning techniques to problems in bioinformatics and materials science, reflecting their engagement with diverse scientific domains.

Best Publications

SignalP 5.0 improves signal peptide predictions using deep neural networks

Jose Juan Almagro Armenteros;Konstantinos D. Tsirigos;Casper Kaae Sønderby;Thomas Nordahl Petersen

4315
Citations
Autoencoding beyond pixels using a learned similarity metric

Anders Boesen Lindbo Larsen;Søren Kaae Sønderby;Hugo Larochelle;Ole Winther

1834
Citations
DeepLoc: prediction of protein subcellular localization using deep learning.

Jose Juan Almagro Armenteros;Jose Juan Almagro Armenteros;Casper Kaae Sønderby;Søren Kaae Sønderby;Henrik Nielsen

1140
Citations
Detecting sequence signals in targeting peptides using deep learning.

Jose Juan Almagro Armenteros;Marco Salvatore;Marco Salvatore;Olof Emanuelsson;Olof Emanuelsson;Ole Winther;Ole Winther;Ole Winther

1007
Citations
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update

Jan Christian Bryne;Eivind Valen;Man-Hung Eric Tang;Troels Torben Marstrand

840
Citations
Improved metagenome binning and assembly using deep variational autoencoders

Jakob Nybo Nissen;Jakob Nybo Nissen;Joachim Johansen;Rosa Lundbye Allesøe;Casper Kaae Sønderby

644
Citations
NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

Michael Schantz Klausen;Martin Closter Jespersen;Henrik Nielsen;Kamilla Kjærgaard Jensen

614
Citations
Ladder Variational Autoencoders

Casper Kaae Sønderby;Tapani Raiko;Lars Maaløe;Søren Kaae Sønderby

565
Citations
The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

Harukazu Suzuki;Alistair R.R. Forrest;Erik Van Nimwegen;Carsten O. Daub

497
Citations
Auxiliary deep generative models

Lars Maaløe;Casper Kaae Sønderby;Søren Kaae Sønderby;Ole Winther

417
Citations
BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis.

Frederik Otzen Bagger;Damir Sasivarevic;Sina Hadi Sohi;Linea Gøricke Laursen

367
Citations
Gaussian Processes for Classification: Mean-Field Algorithms

Manfred Opper;Ole Winther

345
Citations
Sequential Neural Models with Stochastic Layers

Marco Fraccaro;Søren Kaae Sønderby;Ulrich Paquet;Ole Winther

329
Citations
scVAE: variational auto-encoders for single-cell gene expression data.

Christopher Heje Grønbech;Christopher Heje Grønbech;Christopher Heje Grønbech;Maximillian Fornitz Vording;Pascal Timshel;Casper Kaae Sønderby

292
Citations
Bayesian Non-negative Matrix Factorization

Mikkel N. Schmidt;Ole Winther;Lars Kai Hansen

276
Citations
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

Marco Fraccaro;Simon Due Kamronn;Ulrich Paquet;Ole Winther

268
Citations
Expectation Consistent Approximate Inference

Manfred Opper;Ole Winther

260
Citations
NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data.

Alessandro Montemurro;Viktoria Schuster;Helle Rus Povlsen;Amalie Kai Bentzen

238
Citations
Mean-field approaches to independent component analysis

Pedro A. D. F. R. Højen-Sørensen;Ole Winther;Lars Kai Hansen

218
Citations
Convolutional LSTM Networks for Subcellular Localization of Proteins

SØren Kaae SØnderby;Casper Kaae SØnderby;Henrik Nielsen;Ole Winther

205
Citations
Genome-wide detection and analysis of hippocampus core promoters using DeepCAGE

Eivind Valen;Giovanni Pascarella;Alistair Morgan Chalk;Norihiro Maeda

162
Citations
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

Lars Maaløe;Marco Fraccaro;Valentin Liévin;Ole Winther

118
Citations

Frequent Co-Authors

Manfred Opper Technical University of Berlin

Lars Kai Hansen Technical University of Denmark

Bernard Henri Fleury Aalborg University

Anders Krogh University of Copenhagen

Bo T. Porse University of Copenhagen

Lars Vedel Kessing University of Copenhagen

Jakob E. Bardram Technical University of Denmark

Finn Cilius Nielsen Copenhagen University Hospital

Albin Sandelin University of Copenhagen

Vladimir Brusic University of Nottingham Ningbo China

External Links

Personal website of Ole Winther

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