title: Using ensemble methods to deal with imbalanced data in predicting protein-protein interactions.
abstract: In proteins, the number of interacting pairs is usually much smaller than the number of non-interacting ones. So the imbalanced data problem will arise in the field of protein-protein interactions (PPIs) prediction. In this article, we introduce two ensemble methods to solve the imbalanced data problem. These ensemble methods combine the based-cluster under-sampling technique and the fusion classifiers. And then we evaluate the ensemble methods using a dataset from Database of Interacting Proteins (DIP) with 10-fold cross validation. All the prediction models achieve area under the receiver operating characteristic curve (AUC) value about 95%. Our results show that the ensemble classifiers are quite effective in predicting PPIs; we also gain some valuable conclusions on the performance of ensemble methods for PPIs in imbalanced data. The prediction software and all dataset employed in the work can be obtained for free at http://cic.scu.edu.cn/bioinformatics/Ensemble_PPIs/index.html.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, PR China.
authors.forename: Yongqing
authors.initials: Y
authors.lastname: Zhang
authors.forename: Danling
authors.initials: D
authors.lastname: Zhang
authors.forename: Gang
authors.initials: G
authors.lastname: Mi
authors.forename: Daichuan
authors.initials: D
authors.lastname: Ma
authors.forename: Gongbing
authors.initials: G
authors.lastname: Li
authors.forename: Yanzhi
authors.initials: Y
authors.lastname: Guo
authors.forename: Menglong
authors.initials: M
authors.lastname: Li
authors.forename: Min
authors.initials: M
authors.lastname: Zhu
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D025941:Protein Interaction Mapping; D012372:ROC Curve; D060388:Support Vector Machine