Predicting pathogen evolution and immune evasion in the age of artificial intelligence
The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants afte...
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| Language: | English |
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Elsevier
2025-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001138 |
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| author | D.J. Hamelin M. Scicluna I. Saadie F. Mostefai J.C. Grenier C. Baron E. Caron J.G. Hussin |
| author_facet | D.J. Hamelin M. Scicluna I. Saadie F. Mostefai J.C. Grenier C. Baron E. Caron J.G. Hussin |
| author_sort | D.J. Hamelin |
| collection | DOAJ |
| description | The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information. |
| format | Article |
| id | doaj-art-72d45f93c66b456888bdb9536c30b0ee |
| institution | DOAJ |
| issn | 2001-0370 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-72d45f93c66b456888bdb9536c30b0ee2025-08-20T03:05:49ZengElsevierComputational and Structural Biotechnology Journal2001-03702025-01-01271370138210.1016/j.csbj.2025.03.044Predicting pathogen evolution and immune evasion in the age of artificial intelligenceD.J. Hamelin0M. Scicluna1I. Saadie2F. Mostefai3J.C. Grenier4C. Baron5E. Caron6J.G. Hussin7Montreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada; Mila - Quebec AI Institute, Montréal, Quebec, Canada; Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, CanadaMontreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada; Mila - Quebec AI Institute, Montréal, Quebec, Canada; Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, CanadaMontreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada; Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, CanadaMontreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada; Mila - Quebec AI Institute, Montréal, Quebec, Canada; Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, CanadaMontreal Heart Institute, Université de Montréal, Montréal, Quebec, CanadaMontreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada; Mila - Quebec AI Institute, Montréal, Quebec, Canada; Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, CanadaCHU Sainte-Justine Research Center, Université de Montréal, Montréal, Quebec, Canada; Yale Center for Immuno-Oncology, Yale Center for Systems and Engineering Immunology, Yale Center for Infection and Immunity, Yale School of Medicine, New Haven, CT, USAMontreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada; Mila - Quebec AI Institute, Montréal, Quebec, Canada; Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada; Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada; Corresponding author at: Montreal Heart Institute, Université de Montréal, Montréal, Quebec, Canada.The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information.http://www.sciencedirect.com/science/article/pii/S2001037025001138Viral evolutionViral forecastingBioinformaticsMachine learningLanguage modelsPandemic preparedness |
| spellingShingle | D.J. Hamelin M. Scicluna I. Saadie F. Mostefai J.C. Grenier C. Baron E. Caron J.G. Hussin Predicting pathogen evolution and immune evasion in the age of artificial intelligence Computational and Structural Biotechnology Journal Viral evolution Viral forecasting Bioinformatics Machine learning Language models Pandemic preparedness |
| title | Predicting pathogen evolution and immune evasion in the age of artificial intelligence |
| title_full | Predicting pathogen evolution and immune evasion in the age of artificial intelligence |
| title_fullStr | Predicting pathogen evolution and immune evasion in the age of artificial intelligence |
| title_full_unstemmed | Predicting pathogen evolution and immune evasion in the age of artificial intelligence |
| title_short | Predicting pathogen evolution and immune evasion in the age of artificial intelligence |
| title_sort | predicting pathogen evolution and immune evasion in the age of artificial intelligence |
| topic | Viral evolution Viral forecasting Bioinformatics Machine learning Language models Pandemic preparedness |
| url | http://www.sciencedirect.com/science/article/pii/S2001037025001138 |
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