RF-PSSM: A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and Human

Abstract

The identification of hepatitis C virus (HCV) virus-human protein interactions will not only help us understand the molecular mechanisms of related diseases but also be conductive to discovering new drug targets. An increasing number of clinically and experimentally validated interactions between HCV and human proteins have been documented in public databases, facilitating studies based on computational methods. In this study, we proposed a new computational approach, rotation forest position-specific scoring matrix (RF-PSSM), to predict the interactions among HCV and human proteins. In particular, PSSM was used to characterize each protein, two-dimensional principal component analysis (2DPCA) was then adopted for feature extraction of PSSM. Finally, rotation forest (RF) was used to implement classification. The results of various ablation experiments show that on independent datasets, the accuracy and area under curve (AUC) value of RF-PSSM can reach 93.74% and 94.29%, respectively, outperforming almost all cutting-edge research. In addition, we used RF-PSSM to predict 9 human proteins that may interact with HCV protein E1, which can provide theoretical guidance for future experimental studies.