Multi-scale convolutional neural networks for the prediction of human-virus protein interactions

Xiaodi Yang, Ziding Zhang, Stefan Wuchty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Allowing the prediction of human-virus protein-protein interactions (PPI), our algorithm is based on a Siamese Convolutional Neural Network architecture (CNN), accounting for pre-acquired protein evolutionary profiles (i.e. PSSM) as input. In combinations with a multilayer perceptron, we evaluate our model on a variety of human-virus PPI datasets and compare its results with traditional machine learning frameworks, a deep learning architecture and several other human-virus PPI prediction methods, showing superior performance. Furthermore, we propose two transfer learning methods, allowing the reliable prediction of interactions in cross-viral settings, where we train our system with PPIs in a source human-virus domain and predict interactions in a target human-virus domain. Notable, we observed that our transfer learning approaches allowed the reliable prediction of PPIs in relatively less investigated human-virus domains, such as Dengue, Zika and SARS-CoV-2.

Original languageEnglish (US)
Title of host publicationICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
EditorsAna Paula Rocha, Luc Steels, Jaap van den Herik
PublisherSciTePress
Pages41-48
Number of pages8
ISBN (Electronic)9789897584848
StatePublished - 2021
Event13th International Conference on Agents and Artificial Intelligence, ICAART 2021 - Virtual, Online
Duration: Feb 4 2021Feb 6 2021

Publication series

NameICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
Volume2

Conference

Conference13th International Conference on Agents and Artificial Intelligence, ICAART 2021
CityVirtual, Online
Period2/4/212/6/21

Keywords

  • CNN
  • Deep learning
  • Human-virus PPI
  • Prediction
  • PSSM
  • Transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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