The Australian e-Health Research Centre, Health and Biosecurity, CSIRO.
Stud Health Technol Inform. 2024 Sep 24;318:150-155. doi: 10.3233/SHTI240907.
Antimicrobial resistance (AMR) poses a significant global health threat, resulting in 4.96 million deaths in 2019, with projections reaching 10 million by 2050. This resistance, primarily due to the overuse of antibiotics, complicates the treatment of infections caused by various microorganisms, including the gram-negative bacterium Escherichia coli. Traditional culture-based methods for detecting AMR are slow and imprecise, hindering timely clinical decision-making. In contrast, whole genome sequencing offers a faster, more accurate alternative for AMR detection. A novel machine learning study leveraging whole genomic sequencing data to predict the phenotypic susceptibility of Escherichia coli to ciprofloxacin is presented. Using a novel dataset of 256 bacterial genomes and related susceptibility data, features were generated based on AMRFinderPlus findings and k-mer frequencies. The machine learning models, Random Forest and XGBoost, were evaluated using a five-fold cross-validation approach. Results showed that combining AMRFinderPlus and k-mer frequency features could achieve more than 90% accuracy using the XGBoost gradient boosting model. These findings suggest that the best results may be achieved using reference-free features combined with known gene markers.
抗菌药物耐药性（AMR）对全球健康构成重大威胁，2019 年导致 496 万人死亡，预计到 2050 年将达到 1000 万人。这种耐药性主要是由于抗生素的过度使用，使得各种微生物引起的感染（包括革兰氏阴性菌大肠杆菌）的治疗变得复杂。传统的基于培养的方法检测 AMR 速度慢且不精确，阻碍了及时的临床决策。相比之下，全基因组测序为 AMR 检测提供了更快、更准确的替代方法。本文提出了一种利用全基因组测序数据预测大肠杆菌对环丙沙星表型敏感性的新型机器学习研究。使用包含 256 个细菌基因组和相关药敏数据的新型数据集，基于 AMRFinderPlus 发现和 k-mer 频率生成特征。使用五重交叉验证方法评估了机器学习模型随机森林和 XGBoost。结果表明，使用 XGBoost 梯度提升模型，结合 AMRFinderPlus 和 k-mer 频率特征可以达到 90%以上的准确率。这些发现表明，使用无参考特征结合已知基因标记可能会取得最佳结果。
