The amount of biologic data produced by biomedical research has increased significantly in both volume and complexity in recent years. Advances in computational power have increasingly enabled the use of machine learning (ML) to analyze and predict patterns from large-scale, complex biologic datasets. In nonhuman primate (NHP) infectious disease models, such high-dimensional datasets containing a large number of features are often obtained by next-generation sequencing-based multiomics and immunologic analyses. As a result, ML is particularly valuable for effective analysis and predictive modeling in this context. This review demonstrates that the application of ML in NHP infectious disease models has increased over time. Ensemble methods, particularly random forest, have emerged as the most frequently used algorithms, followed by regression and clustering approaches. Logistic regression and hierarchical clustering were the most commonly applied regression and clustering methods, respectively. These techniques are primarily used for vaccine response prediction, biomarker discovery, disease progression analysis, gene and pathway identification, and immune response characterization. Despite this increasing trend, the overall adoption of ML in NHP infectious disease models remains limited, which may reflect gaps in familiarity and computational expertise among researchers. Recent advances in generative artificial intelligence and user-friendly analytical platforms are expected to improve accessibility and promote broader adoption. This review aims to support understanding and facilitate wider application of ML in NHP infectious disease models.
Koo et al. (Mon,) studied this question.