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Magnetic Resonance in Medicine, ISSN 0740-3194, 03/2015, Volume 73, Issue 3, pp. 1171 - 1176
Journal Article
Bioinformatics (Oxford, England), ISSN 1367-4803, 11/2017, Volume 33, Issue 21, pp. 3387 - 3395
Motivation: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics... 
SUBSTITUTION MATRICES | BIOTECHNOLOGY & APPLIED MICROBIOLOGY | WEB SERVER | BIOCHEMICAL RESEARCH METHODS | MATHEMATICAL & COMPUTATIONAL BIOLOGY | ACCURACY
Journal Article
NATURE BIOTECHNOLOGY, ISSN 1087-0156, 04/2019, Volume 37, Issue 4, pp. 420 - 420
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes.... 
TOPOLOGY | SEC | TRANSLOCATION | BIOTECHNOLOGY & APPLIED MICROBIOLOGY | GENERATION | MODEL | IDENTIFICATION | PROTEINS | Proteins | Domains | Membranes | Peptides | Acids | Neural networks | Signal peptides | Biological Sciences | Naturvetenskap | Natural Sciences | Biologiska vetenskaper
Journal Article
Bioinformatics, ISSN 1367-4803, 11/2017, Volume 33, Issue 22, pp. 3685 - 3690
Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during... 
SEGMENTATION | PROTEIN SECONDARY STRUCTURE | BIOTECHNOLOGY & APPLIED MICROBIOLOGY | BIOCHEMICAL RESEARCH METHODS | MATHEMATICAL & COMPUTATIONAL BIOLOGY | PREDICTION | Review
Journal Article
Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, 06/2019, Volume 87, Issue 6, pp. 520 - 527
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... 
secondary structure | deep learning | disorder | solvent accessibility | local structure prediction | BIOPHYSICS | BIOCHEMISTRY & MOLECULAR BIOLOGY | ANGLES | Proteins | Neural networks | Analysis | Solvents | Accuracy | Accessibility | Run time (computers) | Machine learning | Predictions | Amino acid sequence | Secondary structure | Protein structure
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2015, Volume 9199, pp. 68 - 80
Algorithms for Computational Biology 9199 (2015) 68 Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or... 
Deep learning | LSTM | RNN | Neural networks | Machine learning | Subcellular location | Convolutional networks
Conference Proceeding
02/2016
Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We... 
Journal Article
09/2015
We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We... 
Computer Science - Computer Vision and Pattern Recognition
Journal Article
02/2016
Variational Autoencoders are powerful models for unsupervised learning. However deep models with several layers of dependent stochastic variables are difficult... 
Journal Article
10/2016
Most existing Neural Machine Translation models use groups of characters or whole words as their unit of input and output. We propose a model with a... 
Computer Science - Computation and Language
Journal Article
10/2016
Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled... 
Journal Article
Proceedings of the 8th ACM International Conference on bioinformatics, computational biology,and health informatics, 08/2017, pp. 73 - 78
Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile.... 
conditional random field | recurrent neural network | secondary protein structure | Recurrent neural network | Secondary protein structure | Conditional random field
Conference Proceeding
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