Xiao-Wei Zhao, Zhi-Qiang Ma and Ming-Hao Yin Pages 492 - 500 ( 9 )
Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.
Protein-protein interactions, principal component analysis (PCA), support vector machine (SVM), protein sequences, prediction
School of Computer Science, Northeast Normal University, Changchun, 130117, P.R. China.