Journal
BIOINFORMATICS
Volume 33, Issue 24, Pages 3909-3916Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btx496
Keywords
-
Categories
Funding
- National Institutes of Health [R01-GM100701]
- National Science Foundation [CNS-1429294]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1429294] Funding Source: National Science Foundation
Ask authors/readers for more resources
Motivation: Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. Results: We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available