Integrating multi-species and multi-antibiotic resistance classification with MALDI-TOF: a deep learning approach to predict AMR

Enhanced classification and identification of bacterial and viral microorganisms by integration of MALDI-TOF mass spectrometry with artificial intelligence

The Extra Trees Classifier consistently achieved the highest average accuracy and F1-score in both Gram type classification and species-level identification, demonstrating superior generalization across datasets.

Machine learning methods in predicting antimicrobial resistance

Antimicrobial resistance poses a significant global threat to healthcare systems worldwide. The high cost and short lifespan of new antibiotics due to the rapid evolution of resistance exacerbate this crisis, prompting exploration of diverse strategies to combat AMR. Within the context of AMR diagnostics, machine learning approaches for predicting bacterial resistance are under active investigation. This paper reviews state-of-the-art methods for predicting antibiotic resistance in individual bacterial isolates. The review encompasses both traditional ML algorithms, such as logistic regression and random forests, and more advanced techniques including convolutional and graph neural networks, as well as transformer language models. The inherent high variability of bacterial genomes and the heterogeneity of mutational processes hinder model generalizability, while variations in clinical conditions during strain development contribute to prediction instability on new datasets. This necessitates further research into improved data collection and preprocessing methods, as well as a more comprehensive consideration of both known and emerging resistance mechanisms.

An Interpretable Multi-Objective Machine Learning Framework for In Silico Prioritization of Anti-Staphylococcus aureus Antimicrobial Peptides

An interpretable, honestly benchmarked multi-objective pipeline that optimizes known anti-S.

Deep Learning-Based Prediction of Antimicrobial Resistance

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.

MALDI-ST: A deep learning-based framework for rapid bacterial strain typing using MALDI-TOF mass spectra

A proof-of-concept study demonstrates the potential of MALDI-TOF MS for bacterial strain typing across four key pathogens, and reveals that center- and instrument-specific variation can substantially affect performance.

ARGai 3.0: AI fusion approach to identify antibiotic-resistant strains in E. coli using high-throughput whole genome sequencing data