DeepLoc: prediction of protein subcellular localization using deep learning
By José Juan Almagro Armenteros, Casper Kaae Sønderby, Søren Kaae Sønderby, Henrik Nielsen, Ole Winther. 2017
173 citations recorded by the source corpus.
Subfields: Biophysics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
- Michael Heinzinger
- Ahmed Elnaggar
- Christian Dallago
- Yu Wang
- Dmitrii Nachaev
- Casper Kaae Sønderby
- Henrik Nielsen
- Ole Winther
In Molecular Biology
- Burkhard Rost
- Michael Heinzinger
- Henrik Nielsen
- Christian Dallago
- Ahmed Elnaggar
- Yu Wang
- Arne Elofsson
- Florian Matthes
Citations in the corpus
- NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “Since then, deep learning techniques have affected the application of machine learning in biology expanding the ability of prediction tools to produce more accurate results on complex datasets (9-16).”
- ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing (2020, methodology): “A redundancy reduced version (30%) of the DeepLoc [26] dataset was used to assess whether the LM learnt to classify proteins into membrane-bound and water-soluble (Panel B) or according to their cellular compartment (Panel C).”
- Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “[39] and encodings based on single sequences (ProtVec [64] and our SeqVec).”
- Detecting sequence signals in targeting peptides using deep learning (2019, methodology): “Today, new methods, such as DeepLoc (Almagro Armenteros et al, 2017), use bidirectional LSTM (BiLSTM) to predict the localisation of proteins to a broader range of compartments.”