Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment
Abstract Background Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission...
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BMC
2025-04-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-025-11589-8 |
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| author | Sangwook Kim Min-Ah Kim Bitgoeul Kim Jisu Lee Se-Kyung Jung Jonghong Kim Ho-Young Chung Chung-Young Lee Sungmoon Jeong |
| author_facet | Sangwook Kim Min-Ah Kim Bitgoeul Kim Jisu Lee Se-Kyung Jung Jonghong Kim Ho-Young Chung Chung-Young Lee Sungmoon Jeong |
| author_sort | Sangwook Kim |
| collection | DOAJ |
| description | Abstract Background Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission. A comprehensive risk assessment framework based on PB2 sequences is necessary, which should encompass detailed analyses of specific residues and mutations while maintaining sufficient generality for application to non-PB2 segments. Results In this study, we developed two complementary approaches: a regression-based model for accurately distinguishing among risk groups, and a SHAP-based risk assessment model for more meaningful risk analyses. For the regression-based risk models, we compared various methodologies, including tree ensemble methods, conventional regression models, and deep learning architectures. The optimized regression model, combined with SHAP value analysis, identified and ranked individual residues contributing to zoonotic potential. The SHAP-based risk model enabled intra-class analyses within the zoonotic risk assessment framework and quantified risk yields from specific mutations. Conclusion Experimental analyses demonstrated that the Random Forest regression model outperformed other models in most cases, and we validated the target value settings for risk regression through ablation studies. Our SHAP-based analysis identified key residues (271A, 627K, 591R, 588A, 292I, 684S, 684A, 81M, 199S, and 368Q) and mutations (T271A, Q368R/K, E627K, Q591R, A588T/I/V, and I292V/T) critical for zoonotic risk assessment. Using the SHAP-based risk assessment model, we found that influenza A viruses from Phasianidae showed elevated zoonotic risk scores compared to those from other avian species. Additionally, mutations I292V/T, Q368R, A588T/I, V598A/I/T, and E/V627K were identified as significant mutations in the Phasianidae. These PB2-focused quantitative methods provide a robust and generalizable framework for both rapid screening of avians’ zoonotic potential and analytical quantification of risks associated with specific residues or mutations. |
| format | Article |
| id | doaj-art-bec00976a0b648d78deb3ad76d223037 |
| institution | OA Journals |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
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| series | BMC Genomics |
| spelling | doaj-art-bec00976a0b648d78deb3ad76d2230372025-08-20T02:19:07ZengBMCBMC Genomics1471-21642025-04-0126111410.1186/s12864-025-11589-8Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segmentSangwook Kim0Min-Ah Kim1Bitgoeul Kim2Jisu Lee3Se-Kyung Jung4Jonghong Kim5Ho-Young Chung6Chung-Young Lee7Sungmoon Jeong8Bio-medical Research Institute, Kyungpook National University HospitalDepartment of Microbiology, School of Medicine, Kyungpook National UniversityDepartment of Microbiology, School of Medicine, Kyungpook National UniversityDepartment of Microbiology, School of Medicine, Kyungpook National UniversityDepartment of Microbiology, School of Medicine, Kyungpook National UniversityDepartment of Neurology, Keimyung University Dongsan Medical CenterDepartment of Medical Informatics, School of Medicine, Kyungpook National UniversityDepartment of Microbiology, School of Medicine, Kyungpook National UniversityDepartment of Medical Informatics, School of Medicine, Kyungpook National UniversityAbstract Background Influenza A virus (IAV) is a major global health threat, causing seasonal epidemics and occasional pandemics. Particularly, Influenza A viruses from avian species pose significant zoonotic threats, with PB2 adaptation serving as a critical first step in cross-species transmission. A comprehensive risk assessment framework based on PB2 sequences is necessary, which should encompass detailed analyses of specific residues and mutations while maintaining sufficient generality for application to non-PB2 segments. Results In this study, we developed two complementary approaches: a regression-based model for accurately distinguishing among risk groups, and a SHAP-based risk assessment model for more meaningful risk analyses. For the regression-based risk models, we compared various methodologies, including tree ensemble methods, conventional regression models, and deep learning architectures. The optimized regression model, combined with SHAP value analysis, identified and ranked individual residues contributing to zoonotic potential. The SHAP-based risk model enabled intra-class analyses within the zoonotic risk assessment framework and quantified risk yields from specific mutations. Conclusion Experimental analyses demonstrated that the Random Forest regression model outperformed other models in most cases, and we validated the target value settings for risk regression through ablation studies. Our SHAP-based analysis identified key residues (271A, 627K, 591R, 588A, 292I, 684S, 684A, 81M, 199S, and 368Q) and mutations (T271A, Q368R/K, E627K, Q591R, A588T/I/V, and I292V/T) critical for zoonotic risk assessment. Using the SHAP-based risk assessment model, we found that influenza A viruses from Phasianidae showed elevated zoonotic risk scores compared to those from other avian species. Additionally, mutations I292V/T, Q368R, A588T/I, V598A/I/T, and E/V627K were identified as significant mutations in the Phasianidae. These PB2-focused quantitative methods provide a robust and generalizable framework for both rapid screening of avians’ zoonotic potential and analytical quantification of risks associated with specific residues or mutations.https://doi.org/10.1186/s12864-025-11589-8Influenza A virusAvian influenza virusPB2Artificial intelligenceMachine learningSHAP |
| spellingShingle | Sangwook Kim Min-Ah Kim Bitgoeul Kim Jisu Lee Se-Kyung Jung Jonghong Kim Ho-Young Chung Chung-Young Lee Sungmoon Jeong Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment BMC Genomics Influenza A virus Avian influenza virus PB2 Artificial intelligence Machine learning SHAP |
| title | Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment |
| title_full | Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment |
| title_fullStr | Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment |
| title_full_unstemmed | Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment |
| title_short | Machine learning assessment of zoonotic potential in avian influenza viruses using PB2 segment |
| title_sort | machine learning assessment of zoonotic potential in avian influenza viruses using pb2 segment |
| topic | Influenza A virus Avian influenza virus PB2 Artificial intelligence Machine learning SHAP |
| url | https://doi.org/10.1186/s12864-025-11589-8 |
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