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Updated: Jun 21, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

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Published on: October 27, 2023

Machine Learning in Nonhuman Primate Models of Infectious Diseases: Current Applications and Future Perspectives.

Bon-Sang Koo^1,2, Remco A Nederlof³, Eunsu Jeon¹

¹National Primate Research Center, Korea Research Institute of Bioscience and Biotechnology, Cheongju, Republic of Korea.

Journal of the American Association for Laboratory Animal Science : JAALAS

|June 19, 2026

Summary

This summary is machine-generated.

Machine learning (ML) is increasingly used in nonhuman primate (NHP) infectious disease models for analyzing complex biologic data. While adoption is growing, further accessibility is needed for broader application in NHP research.

Keywords:

AI, artificial intelligence ANN, artificial neural network CNN, convolutional neural network DBSCAN, density-based spatial clustering of applications with noise DNN, deep neural network FCM, fuzzy c-means GNN, graph neural network KNN, k-nearest neighbors LASSO, least absolute shrinkage and selection operator LDA, linear discriminant analysis ML, machine learning MLP, multilayer perceptron NHP, nonhuman primate PCA, principal component analysis QDA, quadratic discriminant analysis RNN, recurrent neural network SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 SIV, simian immunodeficiency virus SVM, support vector machine UMAP, uniform manifold approximation and projection XGBoost, extreme gradient boosting t-SNE, t-distributed stochastic neighbor embedding

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Area of Science:

Biomedical research
Computational biology
Infectious disease modeling

Background:

Biomedical research generates vast, complex biologic data.
Machine learning (ML) offers powerful tools for analyzing large datasets.
Nonhuman primate (NHP) infectious disease models produce high-dimensional multiomics and immunologic data.

Purpose of the Study:

To review the application and trends of ML in NHP infectious disease models.
To identify commonly used ML algorithms and their applications.
To highlight challenges and future directions for ML adoption in this field.

Main Methods:

Systematic review of ML applications in NHP infectious disease research.
Analysis of algorithm usage, including ensemble methods, regression, and clustering.
Identification of primary research areas utilizing ML.

Main Results:

ML application in NHP models has increased over time.
Ensemble methods (random forest) are most common, followed by regression (logistic regression) and clustering (hierarchical clustering).
ML is used for vaccine response prediction, biomarker discovery, disease progression, and immune characterization.

Conclusions:

ML is a valuable tool for NHP infectious disease research, with growing adoption.
Limited familiarity and computational expertise hinder wider ML use.
Generative AI and user-friendly platforms are expected to increase accessibility and adoption.