DeepLoc: prediction of protein subcellular localization using deep learning
University of Copenhagen · Technical University of Denmark
Abstract
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 methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. RESULTS: Here, we present a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information. At its core, the…
Citation impact
- FWCI
- 34.20
- Percentile
- 100%
- References
- 28
Authors
5- JJJosé Juan Almagro ArmenterosCorresponding
University of Copenhagen, Technical University of Denmark
- CKCasper Kaae Sønderby
University of Copenhagen
- SKSøren Kaae Sønderby
University of Copenhagen
- HNHenrik Nielsen
Technical University of Denmark
- OWOle Winther
University of Copenhagen, Technical University of Denmark
Topics & keywords
- Artificial intelligence
- Computer science
- Subcellular localization
- Deep learning
- Protein subcellular localization prediction
- Pattern recognition (psychology)
- Machine learning
- Computational biology