Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem
Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The applica...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1499079/full |
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| author | Jonathan Hare Morten Nielsen Agnes Kiragga Daniel Ochiel |
| author_facet | Jonathan Hare Morten Nielsen Agnes Kiragga Daniel Ochiel |
| author_sort | Jonathan Hare |
| collection | DOAJ |
| description | Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure. |
| format | Article |
| id | doaj-art-ce7cc9bef8e341c78b5860030c8a6d89 |
| institution | OA Journals |
| issn | 1663-9812 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-ce7cc9bef8e341c78b5860030c8a6d892025-08-20T01:58:24ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122024-12-011510.3389/fphar.2024.14990791499079Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystemJonathan Hare0Morten Nielsen1Agnes Kiragga2Daniel Ochiel3Biolife Research Limited, Nairobi, KenyaDepartment of Health Technology, Technical University of Denmark, Lyngby, DenmarkData Science Program, Africa Population Health Centre, Nairobi, KenyaHenry Jackson Foundation Medical Research International, Nairobi, KenyaArtificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.https://www.frontiersin.org/articles/10.3389/fphar.2024.1499079/fullmachine learning (ML)artiificial intelligencevaccinesAfricadrug discovery |
| spellingShingle | Jonathan Hare Morten Nielsen Agnes Kiragga Daniel Ochiel Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem Frontiers in Pharmacology machine learning (ML) artiificial intelligence vaccines Africa drug discovery |
| title | Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem |
| title_full | Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem |
| title_fullStr | Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem |
| title_full_unstemmed | Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem |
| title_short | Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem |
| title_sort | sustainable integration of artificial intelligence and machine learning approaches within the african infectious disease vaccine research and development ecosystem |
| topic | machine learning (ML) artiificial intelligence vaccines Africa drug discovery |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1499079/full |
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