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

SUBMITTER: Yang X 

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

REPOSITORIES: biostudies-literature