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Protein features fusion using attributed network embedding for predicting protein-protein interaction.

Mei-Yuan Cao1, Suhaila Zainudin2, Kauthar Mohd Daud2

  • 1Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia. p116930@siswa.ukm.edu.my.

BMC Genomics
|May 13, 2024
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Summary
This summary is machine-generated.

This study introduces FFANE, a novel method combining protein structure and sequence data for accurate protein-protein interaction (PPI) prediction. FFANE significantly improves PPI prediction accuracy across multiple species.

Keywords:
Feature fusion learningGaussian kernelLevenshtein distanceProtein sequencesProtein-protein interaction prediction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular functions and drug discovery.
  • Experimental PPI determination is resource-intensive and limited.
  • Accurate PPI prediction is essential for biological understanding.

Purpose of the Study:

  • To develop a novel computational method for precise protein-protein interaction (PPI) prediction.
  • To enhance PPI prediction accuracy by integrating diverse protein data.
  • To overcome limitations of experimental PPI determination.

Main Methods:

  • Introduced FFANE, a node representation method using initial information fusion.
  • Amalgamated PPI networks and protein sequence data.
  • Utilized Gaussian kernel similarity for structural resemblance and Levenshtein distance for sequence similarity.
  • Employed Stacked Autoencoder (SAE) for feature encoding and learning.
  • Trained classification models on fused features for PPI prediction.

Main Results:

  • Achieved high average accuracies using 5-fold cross-validation on SVM.
  • 94.28% accuracy on Saccharomyces cerevisiae dataset.
  • 97.69% accuracy on Homo sapiens dataset.
  • 84.05% accuracy on Helicobacter pylori dataset.

Conclusions:

  • The FFANE method demonstrates superior efficacy in PPI prediction.
  • Fusion feature representation approach is validated across diverse datasets.
  • Highlights potential value in bioinformatics and computational drug design.