ReviewTropical diseases, travel medicine and vaccines2025
From data to immunity: the role of machine learning in advancing malaria vaccine research: a scoping review.
Review in Tropical diseases, travel medicine and vaccines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMalaria remains a significant global health burden, necessitating the development of effective vaccines. Traditional vaccine development is challenged by the complexity of the Plasmodium parasite and lengthy empirical processes. Machine learning (ML) offers a promising avenue to accelerate and enhance vaccine research.
aimThis review synthesizes recent advances in the application of ML to malaria vaccine research, focusing on immunological signature identification, antigen discovery, and predictive modeling of vaccine efficacy, to highlight its transformative potential.
methodsA targeted literature search was conducted for peer-reviewed articles, reviews, and systematic analyses published between 2017 and 2025. Studies directly addressing ML or AI in malaria vaccine development were included. Data extraction covered ML methodologies, data types, applications, validation strategies, challenges, and limitations. Thematic analysis categorized findings, and a quality assessment ensured methodological rigor.
resultsThematic analysis identified five key areas: (1) antigen discovery and prioritization using supervised and semi-supervised learning; (2) immune signature identification and efficacy prediction via diverse ML algorithms; (3) computational tool and framework development for data integration; (4) broad reviews of AI/ML applications; and (5) epidemiological modeling for policy support. Most studies were conducted in Europe and North America, often with collaborations in Africa.
conclusionML is transforming malaria vaccine research by accelerating antigen discovery, enabling precise immune profiling, and predicting vaccine efficacy. Addressing data quality, model interpretability, and validation challenges is crucial for realizing the full potential of ML in developing next-generation malaria vaccines.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.