Active learning-driven approach for label-efficient prioritization of Antibody-Mediated Protection Against Malaria

Malaria-attributable mortality remains heavily concentrated among children under five years of age, highlighting a persistent and profound vulnerability within this demographic group. Albeit RTS,S/AS01 provides only modest efficacy, the development of safe, high-efficacy, and cost-effective vaccines becomes imperative. Consequently, novel prevention strategies—particularly antibody-based interventions—emerges as a critical priority for reducing global malaria morbidity and mortality. Here we use an active learning–driven XGBoost framework to enable predictive antibody profiles discovery of antibody features attributed to malaria protection. Our active learning technique integrates detailed physicochemical feature engineering from heavy and light chain CDR3 sequences with participant-level cross-validation to prevent data leakage from within-donor correlations. By iteratively selecting the most informative sequences for labeling, our computational framework significantly reduces experimental burden (44.4%) and is 1.8x more efficient, while maintaining high predictive performance. The resulting model operates on an ultra-efficient computational architecture, yielding protection-state predictions with optimal performance across diverse antibody repertoires. To assess scalability, we extend the active-learning pipeline to larger IGHV–IGHJ feature sets and higher-dimensional repertoire landscapes, achieving robust generalization across trial regimen. Our findings enhance the classification performance by discriminating protection status from underlying antibody profiles while jointly modeling time- and dose-dependent effects. Our active learning-based inference reaches AUPRC of ∼ 0.9385 with 8,503 labeled sequences, compared with 15,305 sequences required under random sampling, indicating a substantial improvement in label efficiency. This approach demonstrates that active-learning–guided gradient-boosted inference can support large-scale immune profiling with minimal labeled data, paving the way for rapid, cost-effective ( > 44%) computational screening strategies for next-generation malaria vaccines and antibody-based interventions.