Emmanuel Nkansah¹, Micheal Abimbola Oladosu²*, Moses Adondua Abah³, Abimbola Mary Oluwajembola², Fwangmun Ezekiel Gushit⁴, Olaide Ayokunmi Oladosu⁵, Adesola Esther Adeneye⁶ and Bukola Oluwaseyi Olufosoye⁷

¹Department of Accounting, Economics and Finance, School of Business, La Sierra University, Riverside, CA, USA
²Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria
³Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University of Wukari, Wukari, Taraba State, Nigeria
⁴Department of Public Health, Faculty of Health Science, Ahmadu Bello University, Zaria, Kaduna State, Nigeria
⁵Department of Computer Science, Faculty of Science and Technology, Babcock University, Ilishan, Nigeria
⁶Department of Biological Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria
⁷Department of Medical Microbiology, Faculty of Medical Laboratory Sciences, Ambrose Alli University, Ekpoma, Edo State, Nigeria

*Corresponding Author: Micheal Abimbola Oladosu, Department of Chemical Sciences, Faculty of Science, Anchor University, Ayobo, Ipaja, Lagos, Nigeria.

Received: November 30, 2025; Published: March 31, 2026

Abstract

The emergence and rapid spread of antimicrobial resistance (AMR) pose a critical threat to global public health, necessitating innovative approaches for pathogen identification and resistance prediction. Machine learning (ML) has revolutionised microbial genomics by enabling rapid, accurate analysis of vast genomic datasets to predict AMR phenotypes and identify pathogens. This review examines recent advances in ML applications for microbial genomics, focusing on supervised and unsupervised learning algorithms, deep learning architectures, and their integration with whole-genome sequencing (WGS) data. We discuss the performance of various ML models, including random forests, support vector machines, convolutional neural networks, and ensemble methods in predicting antimicrobial resistance across different bacterial species. The review highlights challenges in model interpretability, data quality, and clinical implementation while exploring emerging trends in federated learning and transfer learning approaches. Understanding these computational methodologies is essential for developing rapid diagnostic tools and informing antimicrobial stewardship programs in clinical and pharmaceutical settings.

Keywords: Machine Learning; Antimicrobial Resistance; Pathogen Identification; Genomics; Whole-Genome Sequencing; Deep Learning

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Citation

Citation: Micheal Abimbola Oladosu., et al. “Machine Learning Approaches in Microbial Genomics for Pathogen Identification and Antimicrobial Resistance Prediction". Acta Scientific Microbiology 9.4 (2026): 27-40.

Copyright

Copyright: © 2026 Micheal Abimbola Oladosu., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.