Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines

• Published in: Briefings in Bioinformatics 2021-11, Vol.22 (6)
• Main Authors: Mahapatra, Satyajit, Sahu, Sitanshu Sekhar
• Format: Article
• Language: English
• Subjects: Algorithms; Amino Acids; Computational Biology - methods; Databases, Genetic; Humans; Machine Learning; Neural Networks, Computer; Protein Interaction Mapping - methods; Proteins - chemistry; Proteins - metabolism; Reproducibility of Results
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbab255

In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid classifier (FSNN-LGBM) uses the fusion of features derived using pseudo amino acid composition and conjoint triad descriptors. The FSNN extracts the high-level abstraction features from the raw features and LGBM performs the PPI prediction task using these abstraction features. On performing 5-fold cross-validation experiments, the proposed hybrid classifier provides average accuracies of 98.70 and 98.38%, respectively, on the intraspecies PPI data sets of Saccharomyces cerevisiae and Helicobacter pylori. Similarly, the average accuracies for the interspecies PPI data sets of the Human-Bacillus and Human-Yersinia data sets are 98.52 and 97.40%, respectively. Compared with the existing methods, the hybrid classifier achieves higher prediction accuracy on the independent test sets and network data sets. The improved prediction performance obtained by the FSNN-LGBM makes it a flexible and effective PPI prediction model.