User profiles for Ole Winther
Ole WintherBiology, Univ of Copenhagen, Genomic Medicine, Rigshospitalet and Technical University … Verified email at bio.ku.dk Cited by 23764 |
SignalP 5.0 improves signal peptide predictions using deep neural networks
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly
synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can …
synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can …
Ladder variational autoencoders
Variational autoencoders are powerful models for unsupervised learning. However deep
models with several layers of dependent stochastic variables are difficult to train which limits …
models with several layers of dependent stochastic variables are difficult to train which limits …
NetSurfP‐2.0: Improved prediction of protein structural features by integrated deep learning
…, MOA Sommer, O Winther… - Proteins: Structure …, 2019 - Wiley Online Library
The ability to predict local structural features of a protein from the primary sequence is of
paramount importance for unraveling its function in absence of experimental structural …
paramount importance for unraveling its function in absence of experimental structural …
[HTML][HTML] SignalP 6.0 predicts all five types of signal peptides using protein language models
…, MH Gíslason, SI Pihl, KD Tsirigos, O Winther… - Nature …, 2022 - nature.com
Signal peptides (SPs) are short amino acid sequences that control protein secretion and
translocation in all living organisms. SPs can be predicted from sequence data, but existing …
translocation in all living organisms. SPs can be predicted from sequence data, but existing …
Autoencoding beyond pixels using a learned similarity metric
…, H Larochelle, O Winther - … on machine learning, 2016 - proceedings.mlr.press
We present an autoencoder that leverages learned representations to better measure
similarities in data space. By combining a variational autoencoder (VAE) with a generative …
similarities in data space. By combining a variational autoencoder (VAE) with a generative …
JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update
JASPAR is a popular open-access database for matrix models describing DNA-binding
preferences for transcription factors and other DNA patterns. With its third major release, …
preferences for transcription factors and other DNA patterns. With its third major release, …
DeepLoc: prediction of protein subcellular localization using deep learning
Motivation The prediction of eukaryotic protein subcellular localization is a well-studied
topic in bioinformatics due to its relevance in proteomics research. Many machine learning …
topic in bioinformatics due to its relevance in proteomics research. Many machine learning …
DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks
Transmembrane proteins span the lipid bilayer and are divided into two major structural
classes, namely alpha helical and beta barrels. We introduce DeepTMHMM, a deep learning …
classes, namely alpha helical and beta barrels. We introduce DeepTMHMM, a deep learning …
DeepLoc 2.0: multi-label subcellular localization prediction using protein language models
The prediction of protein subcellular localization is of great relevance for proteomics research.
Here, we propose an update to the popular tool DeepLoc with multi-localization prediction …
Here, we propose an update to the popular tool DeepLoc with multi-localization prediction …
Detecting sequence signals in targeting peptides using deep learning
In bioinformatics, machine learning methods have been used to predict features embedded
in the sequences. In contrast to what is generally assumed, machine learning approaches …
in the sequences. In contrast to what is generally assumed, machine learning approaches …