Prediction of Protein-Protein Interaction Sites Combing Sequence Profile and Hydrophobic Information

2018 ◽  
pp. 697-702
Author(s):  
Lili Peng ◽  
Fang Chen ◽  
Nian Zhou ◽  
Peng Chen ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Sequence Profile ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Protein Interaction Sites

scholarly journals Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

2018 ◽  
Vol 35 (14) ◽  
pp. 2395-2402 ◽  
Author(s):  
Xiaoying Wang ◽  
Bin Yu ◽  
Anjun Ma ◽  
Cheng Chen ◽  
Bingqiang Liu ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Protein Interaction ◽  
Feature Selection Method ◽  
Feature Space ◽  
Supplementary Information ◽  
Sequence Profile ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Imbalance Problem ◽  
Ppi Networks

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.

A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites

2015 ◽  
Vol 14 (7) ◽  
pp. 746-760 ◽  
Author(s):  
Zhi-Sen Wei ◽  
Jing-Yu Yang ◽  
Hong-Bin Shen ◽  
Dong-Jun Yu
Keyword(s):  
Random Forests ◽  
Protein Interaction ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Protein Interaction Sites
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