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  

作　　者：Xin Liu Yaping Lu Liang Wang Wei Geng Xinyi Shi Xiao Zhang 

机构地区：[1]School of Medical Informatics and Engineering,Xuzhou Medical University,Xuzhou 221000,China [2]College of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China [3]Laboratory Medicine,Guangdong Provincial People’s Hospital,Guangdong Academy of Medical Sciences,Guangzhou 510080,China [4]Hangzhou Institute for Advanced Study,University of Chinese Academy of Sciences,Hangzhou 310005,China

出　　处：《Big Data Mining and Analytics》2023年第1期21-31,共11页大数据挖掘与分析（英文）

摘　　要：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.

关 键 词：protein-protein interactions hepatitis C virus position specific scoring matrix two-dimensional principal component analysis rotation forest 

分 类 号：R363[医药卫生—病理学] TP391.1[医药卫生—基础医学]