Deep Learning-Based Prediction of Antimicrobial Resistance
TL;DR
The review shows that deep learning has significantly improved the modeling ability for distantly homologous sequences, complex mutation combinations, and heterogeneous data, driving AMR prediction from “database matching” to “learnable representations,” and from “single-label discrimination” to “multi-task, multi-representation, and multimodal fusion.”
Abstract
Antimicrobial resistance (AMR) has become a significant challenge in global public health. With the development of whole-genome sequencing, protein language models, graph neural networks, and multimodal learning, deep learning-based AMR prediction research has rapidly evolved from traditional sequence alignment and rule retrieval to representation learning and genotype-phenotype mapping for high-dimensional heterogeneous data. This review systematically summarizes the research progress in this field from three aspects: antimicrobial resistance gene (ARG) identification and classification, genomic mutation-driven resistance phenotype prediction, and non-WGS multimodal extension. The review shows that deep learning has significantly improved the modeling ability for distantly homologous sequences, complex mutation combinations, and heterogeneous data, driving AMR prediction from “database matching” to “learnable representations,” and from “single-label discrimination” to “multi-task, multi-representation, and multimodal fusion.” However, at the same time, problems such as dataset heterogeneity, inconsistent label standards, class imbalance, lineage mixing, insufficient external generalization, and insufficient interpretability still restrict the clinical application of these models. Future research should further strengthen the construction of basic models, standardized evaluation, mining of interpretable mechanisms, and joint modeling of multi-omics and clinical data to promote AMR prediction from method validation to real-world application.