ANOVA‐particle swarm optimization‐based feature selection and gradient boosting machine classifier for improved protein–protein interaction prediction

Feature fusion and selection strategies have been applied to improve accuracy in the prediction of protein–protein interaction (PPI). In this paper, an embedded feature selection framework is developed by integrating a cost function based on analysis of variance (ANOVA) with the particle swarm optim...

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Bibliographic Details
Published in:Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2022-02, Vol.90 (2), p.443-454
Main Authors: Mahapatra, Satyajit, Sahu, Sitanshu Sekhar
Format: Article
Language:English
Subjects:
Accuracy
Amino acid composition
Amino acids
ANOVA
Bacteria - metabolism
Classifiers
Composition
Computational Biology - methods
Cost analysis
Cost function
Datasets
Feature extraction
Feature selection
Humans
light gradient boosting machine
Machine Learning
Particle swarm optimization
Prediction models
Protein Binding
Protein Interaction Mapping
Proteins
Proteins - metabolism
protein–protein interaction
Variance analysis
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Summary:Feature fusion and selection strategies have been applied to improve accuracy in the prediction of protein–protein interaction (PPI). In this paper, an embedded feature selection framework is developed by integrating a cost function based on analysis of variance (ANOVA) with the particle swarm optimization (PSO), termed AVPSO. Initially, the features of the protein sequences extracted using pseudo‐amino acid composition (PseAAC), conjoint triad composition, and local descriptor are fused. Then, AVPSO is employed to select the optimal set of features. The light gradient boosting machine (LGBM) classifier is used to predict the PPIs using the optimal feature subset. On the five‐fold cross‐validation analysis, the proposed model (AVPSO‐LGBM) achieved an average accuracy of 97.12% and 95.09%, respectively, on the intraspecies PPI datasets Saccharomyces cerevisiae and Helicobacter pylori. On the interspecies, PPI datasets of the Human‐Bacillus and Human‐Yersinia, an average accuracy of 95.20% and 93.44%, are achieved. Results obtained on independent test datasets, and network datasets show that the prediction accuracy of the AVPSO‐LGBM is better than the existing methods, demonstrating its generalization ability. The improved prediction performance obtained by the proposed model makes it a reliable and effective PPI prediction model.
ISSN:0887-3585
1097-0134
DOI:10.1002/prot.26236