Xiaoying Wang1,2,3, Bin Yu1,3,4, Anjun Ma5,6, Cheng Chen1,3, Bingqiang Liu2, Qin Ma5,6. 1. College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, China. 2. School of Mathematics, Shandong University, Jinan, China. 3. Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, China. 4. School of Life Sciences, University of Science and Technology of China, Hefei, China. 5. Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA. 6. Department Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
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.
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.
Authors: L Giot; J S Bader; C Brouwer; A Chaudhuri; B Kuang; Y Li; Y L Hao; C E Ooi; B Godwin; E Vitols; G Vijayadamodar; P Pochart; H Machineni; M Welsh; Y Kong; B Zerhusen; R Malcolm; Z Varrone; A Collis; M Minto; S Burgess; L McDaniel; E Stimpson; F Spriggs; J Williams; K Neurath; N Ioime; M Agee; E Voss; K Furtak; R Renzulli; N Aanensen; S Carrolla; E Bickelhaupt; Y Lazovatsky; A DaSilva; J Zhong; C A Stanyon; R L Finley; K P White; M Braverman; T Jarvie; S Gold; M Leach; J Knight; R A Shimkets; M P McKenna; J Chant; J M Rothberg Journal: Science Date: 2003-11-06 Impact factor: 47.728