ArticleNPJ systems biology and applications2024
Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum.
Article in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- IntelligentFrontiers in cellular and infection microbiology · 2026Article
- Mapping the landscape of AI and ML in vaccine innovation: A bibliometric study.Human vaccines & immunotherapeutics · 2025Article
- From data to immunity: the role of machine learning in advancing malaria vaccine research: a scoping review.Tropical diseases, travel medicine and vaccines · 2025Review
- AI-driven epitope prediction: a system review, comparative analysis, and practical guide for vaccine development.NPJ vaccines · 2025Review
- Correlative humoral and cellular immunity to genetically attenuated malaria parasites in humans.iScience · 2025Article
- Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.NPJ systems biology and applications · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Malaria vaccine development is hampered by extensive antigenic variation and complex life stages of Plasmodium species. Vaccine development has focused on a small number of antigens, many of which were identified without utilizing systematic genome-level approaches. In this study, we implement a machine learning-based reverse vaccinology approach to predict potential new malaria vaccine candidate antigens. We assemble and analyze P. falciparum proteomic, structural, functional, immunological, genomic, and transcriptomic data, and use positive-unlabeled learning to predict potential antigens based on the properties of known antigens and remaining proteins. We prioritize candidate antigens based on model performance on reference antigens with different genetic diversity and quantify the protein properties that contribute most to identifying top candidates. Candidate antigens are characterized by gene essentiality, gene ontology, and gene expression in different life stages to inform future vaccine development. This approach provides a framework for identifying and prioritizing candidate vaccine antigens for a broad range of pathogens.
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Registered trials
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