Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information
Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and de...
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| Format: | Article |
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PeerJ Inc.
2025-01-01
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| author | Binghua Li Xin Li Xiaoyu Li Li Wang Jun Lu Jia Wang |
| author_facet | Binghua Li Xin Li Xiaoyu Li Li Wang Jun Lu Jia Wang |
| author_sort | Binghua Li |
| collection | DOAJ |
| description | Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI information. After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. The model achieved an accuracy of 96.89%, precision of 98.79%, recall of 94.85%, F1-score of 96.78%. Finally, we successfully identified 3,269 potential target proteins. Gene ontology (GO) and pathway analysis showed that these genes were highly associated with IAV infection. The analysis of the PPI network further revealed that the predicted proteins were classified as core proteins within the human protein interaction network. This study may encourage the identification of potential targets for the discovery of more effective anti-influenza drugs. The source codes and datasets are available at https://github.com/HVPPIlab/IVA-Human-PPI/. |
| format | Article |
| id | doaj-art-05802a02654a405f8b3b3798436ad28d |
| institution | Kabale University |
| issn | 2167-8359 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj-art-05802a02654a405f8b3b3798436ad28d2025-02-01T15:05:08ZengPeerJ Inc.PeerJ2167-83592025-01-0113e1886310.7717/peerj.18863Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids informationBinghua Li0Xin Li1Xiaoyu Li2Li Wang3Jun Lu4Jia Wang5College of Informatics, Huazhong Agricultural University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan, ChinaInfluenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI information. After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. The model achieved an accuracy of 96.89%, precision of 98.79%, recall of 94.85%, F1-score of 96.78%. Finally, we successfully identified 3,269 potential target proteins. Gene ontology (GO) and pathway analysis showed that these genes were highly associated with IAV infection. The analysis of the PPI network further revealed that the predicted proteins were classified as core proteins within the human protein interaction network. This study may encourage the identification of potential targets for the discovery of more effective anti-influenza drugs. The source codes and datasets are available at https://github.com/HVPPIlab/IVA-Human-PPI/.https://peerj.com/articles/18863.pdfPathogen-host interaction (PHI)Protein-protein interaction (PPI)Influenza A virusXGBoostMachine learningGO and KEGG |
| spellingShingle | Binghua Li Xin Li Xiaoyu Li Li Wang Jun Lu Jia Wang Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information PeerJ Pathogen-host interaction (PHI) Protein-protein interaction (PPI) Influenza A virus XGBoost Machine learning GO and KEGG |
| title | Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information |
| title_full | Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information |
| title_fullStr | Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information |
| title_full_unstemmed | Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information |
| title_short | Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information |
| title_sort | prediction of influenza a virus human protein protein interactions using xgboost with continuous and discontinuous amino acids information |
| topic | Pathogen-host interaction (PHI) Protein-protein interaction (PPI) Influenza A virus XGBoost Machine learning GO and KEGG |
| url | https://peerj.com/articles/18863.pdf |
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