Abstract
Motivation
The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples.
Results
A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively.
Availability and implementation
The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/.
Supplementary information
Supplementary data are available at Bioinformatics online.
2P-243 The prediction of protein-protein interaction sites in sequences using a naive bayes classifier with a kernel density estimation(Bioinformatics:Functional genomics,The 47th Annual Meeting of the Biophysical Society of Japan)
