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ANOVA-particle swarm optimization-based feature selection and gradient boosting machine classifier for improved

Satyajit Mahapatra1, Sitanshu Sekhar Sahu1

  • 1Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, India.

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|September 16, 2021
PubMed
Summary

This study introduces AVPSO-LGBM, a novel method for predicting protein-protein interactions (PPIs). The approach enhances prediction accuracy by fusing features and selecting optimal subsets using analysis of variance (ANOVA) and particle swarm optimization (PSO).

Keywords:
ANOVAlight gradient boosting machineparticle swarm optimizationprotein-protein interaction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Protein-protein interactions (PPIs) are crucial for cellular functions.
  • Accurate prediction of PPIs is essential for understanding biological processes.
  • Existing feature selection methods for PPI prediction have limitations.

Purpose of the Study:

  • To develop an effective feature selection framework for improving PPI prediction accuracy.
  • To integrate feature fusion and selection strategies for enhanced predictive performance.
  • To propose a novel model, AVPSO-LGBM, for reliable PPI prediction.

Main Methods:

  • Feature extraction using pseudo-amino acid composition (PseAAC), conjoint triad composition, and local descriptors.
  • Feature fusion followed by embedded feature selection using AVPSO (ANOVA and PSO integration).
  • Prediction of PPIs using the Light Gradient Boosting Machine (LGBM) classifier on the selected optimal feature subset.

Main Results:

  • The AVPSO-LGBM model achieved high average accuracies: 97.12% (Saccharomyces cerevisiae), 95.09% (Helicobacter pylori), 95.20% (Human-Bacillus), and 93.44% (Human-Yersinia).
  • Demonstrated superior prediction accuracy compared to existing methods on independent and network datasets.
  • Exhibited strong generalization ability, confirming its effectiveness.

Conclusions:

  • The proposed AVPSO-LGBM model is a reliable and effective tool for PPI prediction.
  • The integration of ANOVA-based feature selection with PSO offers significant improvements in prediction accuracy.
  • This method advances the field of computational biology by providing a robust approach to analyzing protein interactions.