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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.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Hu X
PROVIDER: S-EPMC8756175 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
Hu Xiaotian X Feng Cong C Zhou Yincong Y Harrison Andrew A Chen Ming M
Bioinformatics (Oxford, England) 20220101 3
<h4>Motivation</h4>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.<h4>Results</h4>We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple paralle ...[more]