2025/11/13 by Hanqun Cao, César de la Fuente‐Núñez · 1 voice
Biochemistry, Genetics and Molecular Biology · Materials Science · #Bacterial Identification and Susceptibility Testing #Machine Learning in Materials Science #Machine Learning in Bioinformatics
paper · doi:10.1099/mic.0.001629
openalex publication_date 2025/11/13 · openalex created_date 2025/11/13 · openalex updated_date 2026/07/29
Artificial intelligence (AI) and machine learning (ML) are reshaping microbiology, enabling rapid antibiotic discovery, resistance prediction and clinical diagnostics. For microbiologists, the goal is not to build new algorithms but to recognize when ML is appropriate, how to prepare data and how to interpret outputs responsibly. This primer takes that practical stance - driving the ML car rather than rebuilding the engine. At a high level, ML learns from complex patterns, often noisy data. In antibiotic discovery, ML models help identify compounds in biological data and design new ones from scratch using generative AI. In microbiome studies, where measurements are compositional, sparse and often confounded, ML helps uncover community structure and link taxa or functions to phenotypes. In pathogen genomics, supervised models map sequence-derived features (e.g. k‑mers, SNPs and gene presence/absence) to outcomes such as species identity, antimicrobial susceptibility or MIC. Unsupervised learning supports exploration, including clustering, latent gradients and dimensionality reduction for visualization. Across these settings, success hinges less on exotic architectures than on sound problem framing, careful preprocessing and experimental validation.