Prediction of protein-protein interactions based on feature selection and data balancing

Computational approaches are able to analyze protein-protein interactions (ppis) from a different angle of view by complementing the experimental ones. and they are very efficient in determining whether two proteins can interact with each other. in this paper,knns (k-nearest neighbors) is applied to predict the ppis by coding each protein with the physical and chemical properties of its residues,predicted secondary structures and amino acid compositions. mrmr (minimum-redundancy maximum-relevance) feature selection is adopted to select a compact feature set,features of which are considered to be important for the determination of ppi-nesses. because the size of the negative dataset (containing non-interactive protein pairs) is much larger than that of the positive dataset (containing interactive protein pairs),the negative dataset is divided into 5 portions and each portion is combined with the positive dataset for one prediction. thus 5 predictions are performed and the final results are obtained through voting. as a result,the prediction achieves an overall accuracy of 0.8369 with sensitivity of 0.7356. the predictor,developed by this research for the prediction of the fruit fly ppi-nesses,is available for public use at http://chemdata.shu.edu.cn/ppip. © 2013 bentham science publishers.