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Updated: Jun 1, 2025

Modeling The Lifecycle Of Ebola Virus Under Biosafety Level 2 Conditions With Virus-like Particles Containing Tetracistronic Minigenomes
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Supervised learning approaches for predicting Ebola-Human Protein-Protein interactions.

Lopamudra Dey1, Sanjay Chakraborty2

  • 1Department of Biomedical and Clinical Sciences, Linköping University, Sweden; Department of Computer Science & Engineering, Meghnad Saha Institute of Technology, Kolkata, India.

Gene
|January 19, 2025
PubMed
Summary

This study predicts Ebola virus-human protein-protein interactions (PPIs) using machine learning. Deep feed-forward multi-layer perceptron (DMLP) achieved the highest accuracy, identifying 2655 potential human targets.

Keywords:
Deep neural networkEbolaMachine learningMulti-layer perceptronProtein-Protein interactionsViral-host interaction

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning in Virology

Background:

  • Limited protein-protein interaction (PPI) data exists for the Ebola virus.
  • Understanding host-pathogen interactions is crucial for combating viral infections.

Purpose of the Study:

  • To predict novel protein-protein interactions (PPIs) between Ebola virus and human proteins.
  • To develop a comprehensive database (EbolaInt) for Ebola virus PPIs.
  • To identify potential human drug targets for Ebola virus infection.

Main Methods:

  • Creation of a comprehensive Ebola virus-human PPI database (EbolaInt).
  • Utilized sequence-based protein features, including amino acid structure and conjoint triad.
  • Applied supervised machine learning algorithms: K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Deep Feed-Forward Multi-Layer Perceptron (DMLP).

Main Results:

  • The Deep Feed-Forward Multi-Layer Perceptron (DMLP) model demonstrated the highest predictive accuracy.
  • DMLP successfully predicted 2655 potential human target proteins for interaction with Ebola virus proteins.
  • Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses validated the predictions.

Conclusions:

  • Machine learning, particularly DMLP, is effective for predicting Ebola virus-human PPIs.
  • The identified potential targets offer avenues for developing antiviral therapies.
  • The EbolaInt database serves as a valuable resource for future Ebola virus research.