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Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.
Yang, Xiaodi; Yang, Shiping; Lian, Xianyi; Wuchty, Stefan; Zhang, Ziding.
  • Yang X; State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
  • Yang S; State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
  • Lian X; State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China.
  • Wuchty S; Dept. of Computer Science, University of Miami, Miami, FL 33146, USA.
  • Zhang Z; Dept. of Biology, University of Miami, Miami, FL 33146, USA.
Bioinformatics ; 2021 Jul 17.
Article in English | MEDLINE | ID: covidwho-1316806
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ABSTRACT
MOTIVATION 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.

RESULTS:

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 INFORMATION Supplementary data are available at Bioinformatics online.

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Bioinformatics

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Bioinformatics