World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Denmark
2025

D-Index & Metrics

Computer Science

D-Index
57
Citations
23715
World Ranking
3735
National Ranking
13

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

  • Autoencoding beyond pixels using a learned similarity metric

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

  • 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

  • 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

  • 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

  • Improved metagenome binning and assembly using deep variational autoencoders

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

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

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

  • Ladder Variational Autoencoders

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

  • 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

  • Auxiliary deep generative models

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

  • 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

  • Gaussian Processes for Classification: Mean-Field Algorithms

    Manfred Opper;Ole Winther

  • Sequential Neural Models with Stochastic Layers

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

  • 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

  • Bayesian Non-negative Matrix Factorization

    Mikkel N. Schmidt;Ole Winther;Lars Kai Hansen

  • A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

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

  • Expectation Consistent Approximate Inference

    Manfred Opper;Ole Winther

  • 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

  • Mean-field approaches to independent component analysis

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

  • Convolutional LSTM Networks for Subcellular Localization of Proteins

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

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

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

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

    Lars Maaløe;Marco Fraccaro;Valentin Liévin;Ole Winther

Frequent Co-Authors

Manfred Opper
Manfred Opper Technical University of Berlin
Lars Kai Hansen
Lars Kai Hansen Technical University of Denmark
Bernard Henri Fleury
Bernard Henri Fleury Aalborg University
Anders Krogh
Anders Krogh University of Copenhagen
Bo T. Porse
Bo T. Porse University of Copenhagen
Lars Vedel Kessing
Lars Vedel Kessing University of Copenhagen
Jakob E. Bardram
Jakob E. Bardram Technical University of Denmark
Finn Cilius Nielsen
Finn Cilius Nielsen Copenhagen University Hospital
Albin Sandelin
Albin Sandelin University of Copenhagen
Vladimir Brusic
Vladimir Brusic University of Nottingham Ningbo China

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring online degrees related to Computer Science opens up a range of flexible learning options that can lead to rewarding tech careers. Many students consider programs such as the best online physics degree for a strong foundation in analytical skills and problem-solving, both of which are highly valued in computer science fields.

For those interested in the rapidly growing field of data analysis and machine learning, accredited data science programs are widely available online. These programs often focus on practical skills for careers in technology, finance, healthcare, and more.

If your interests lean towards hardware, systems, or network infrastructure, pursuing an electrical engineering degree online admissions option can be an excellent pathway. This allows you to study advanced topics such as embedded systems and telecommunications from anywhere.

Finally, if you’re looking for a quicker entry into the technology workforce, consider 3-month certificate programs that pay well. These certifications can boost your resume and provide industry-aligned skills in a short time—enhancing your job prospects alongside a computer science degree.

Best Scientists Citing Ole Winther

Trending Scientists

Recently Published Articles