Prediction of Protein-Protein Interactions through Deep Learning Based on Sequence Feature Extraction and Interaction Network

Nguyen Quoc Khanh Le, Quang Hien Kha

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

2 Citations (Scopus)

Abstract

Protein-protein interaction (PPI) is an important molecular process in the cell, which is vital to the function of the cell in the biochemical process. This study focuses on human protein. It uses protein information and the relationship of protein interaction network structure to predict PPI. Deep neural network model is implemented to realize PPI prediction. Through five-fold cross-validation, a high performance in the prediction accuracy is produced. The accuracy rate on the test set is 92.45%. To further evaluate the performance of this method, we compared it with other machine learning algorithms. The experimental results show that the method based on neural network is significantly better than the others on the same dataset. It also shows a superior performance compared to previous predictors in this field on PPI prediction.

Original languageEnglish
Title of host publicationBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
Subtitle of host publicationIntelligent Biomedical Systems for a Better Future, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages539-543
Number of pages5
ISBN (Electronic)9781665469173
DOIs
Publication statusPublished - 2022
Event2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022 - Taipei, Taiwan
Duration: Oct 13 2022Oct 15 2022

Publication series

NameBioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings

Conference

Conference2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Country/TerritoryTaiwan
CityTaipei
Period10/13/2210/15/22

Keywords

  • deep learning
  • neural network
  • protein-protein interaction
  • sequence information
  • topological information extraction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Neuroscience (miscellaneous)
  • Instrumentation

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