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Big Data Mining and Analytics 2023, 6(1): 21-31 https://doi.org/10.26599/BDMA.2022.9020031
Open Access | Issue | Published: 24 November 2022

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

Show Author's Information Xin Liu1( ),  Yaping Lu2,  Liang Wang3( ),  Wei Geng1,  Xinyi Shi4,  Xiao Zhang1( )
School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou 221000, China
College of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310005, China
Keywords:
protein-protein interactions, hepatitis C virus, position specific scoring matrix, two-dimensional principal component analysis, rotation forest
Cite this article:
Liu X, Lu Y, Wang L, et al. 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. Big Data Mining and Analytics, 2023, 6(1): 21-31. https://doi.org/10.26599/BDMA.2022.9020031
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Abstract Full text Electronic supplementary material About this article

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.


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About this article

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

Show Author's information Xin Liu1( ), Yaping Lu2, Liang Wang3( ), Wei Geng1, Xinyi Shi4, Xiao Zhang1( )
School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou 221000, China
College of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310005, China

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.

Keywords: protein-protein interactions, hepatitis C virus, position specific scoring matrix, two-dimensional principal component analysis, rotation forest

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Received: 04 July 2022
Revised: 22 August 2022
Accepted: 30 August 2022
Published: 24 November 2022
Issue date: March 2023

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