Dataset Information

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

ABSTRACT: To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e., 'frozen' type and 'fine-tuning' type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Finally, we utilize the 'frozen' type transfer learning approach to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. Supplementary data are available at Bioinformatics online.

SUBMITTER: Yang X

PROVIDER: S-EPMC8406877 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

Yang Xiaodi X Yang Shiping S Lian Xianyi X Wuchty Stefan S Zhang Ziding Z

Bioinformatics (Oxford, England) 20211201 24

<h4>Motivation</h4>To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance.<h4>Results</h4>To predict interactions between human and viral proteins, we combine evolutionary sequence profile featur ...[more]

PMID: 34273146

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Dataset Information

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

Publications

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

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