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
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 HC...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020031 |
| _version_ | 1811296780805996544 |
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| author | Xin Liu Yaping Lu Liang Wang Wei Geng Xinyi Shi Xiao Zhang |
| author_facet | Xin Liu Yaping Lu Liang Wang Wei Geng Xinyi Shi Xiao Zhang |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:53:35Z |
| format | Article |
| id | doaj.art-eced14462cea4726bfda01e5cfc65ff2 |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-04-13T05:53:35Z |
| publishDate | 2023-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-eced14462cea4726bfda01e5cfc65ff22022-12-22T02:59:41ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-03-0161213110.26599/BDMA.2022.9020031RF-PSSM: A Combination of Rotation Forest Algorithm and Position-Specific Scoring Matrix for Improved Prediction of Protein-Protein Interactions Between Hepatitis C Virus and HumanXin Liu0Yaping Lu1Liang Wang2Wei Geng3Xinyi Shi4Xiao Zhang5School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou 221000, ChinaCollege of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, ChinaLaboratory Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaSchool of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou 221000, ChinaHangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310005, ChinaSchool of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou 221000, ChinaThe 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.https://www.sciopen.com/article/10.26599/BDMA.2022.9020031protein-protein interactionshepatitis c virusposition specific scoring matrixtwo-dimensional principal component analysisrotation forest |
| spellingShingle | Xin Liu Yaping Lu Liang Wang Wei Geng Xinyi Shi Xiao Zhang 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 protein-protein interactions hepatitis c virus position specific scoring matrix two-dimensional principal component analysis rotation forest |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | protein-protein interactions hepatitis c virus position specific scoring matrix two-dimensional principal component analysis rotation forest |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020031 |
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