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DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.

Hu, Xiaotian and Feng, Cong and Zhou, Yincong and Harrison, Andrew and Chen, Ming (2022) 'DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.' Bioinformatics, 28 (3). pp. 694-702. ISSN 1367-4803

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Abstract

Motivation Protein–protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. Results We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. Availability and implementation We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git.

Item Type: Article
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 01 Apr 2022 10:19
Last Modified: 01 Apr 2022 10:19
URI: http://repository.essex.ac.uk/id/eprint/32663

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