Affiliation:
1. School of Software Engineering and College of Life Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract
Abstract
Motivation
Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.
Results
This article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units’ models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction.
Availability and implementation
The method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/.
Supplementary information
Supplementary data are available at Bioinformatics online.
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
22 articles.
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