解开复杂的相互作用:机器学习方法预测细菌对抗ZnO和兰添加的ZnO纳米粒子的生存
Diego E Navarro-López1, Yocanxóchitl Perfecto-Avalos1, Araceli Zavala1
1Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Av. Gral Ramón Corona No. 2514, Colonia Nuevo México, Zapopan 45121, Jalisco, Mexico.
Antibiotics (Basel, Switzerland)
|March 27, 2024
概括
机器学习预测了对氧化物纳米颗粒的细菌生存,这是对抗生素耐药细菌的一种有希望的策略. 极端随机树模型实现了95.08%的准确性,识别了关键的预测因素.
科学领域:
- 材料科学 材料科学 材料科学
- 纳米技术纳米技术
- 计算生物学 计算生物学
背景情况:
- 抗生素耐药细菌对全球健康构成重大威胁.
- 金属氧化物纳米粒子提供潜在的抗菌解决方案.
- 了解纳米粒子-细菌相互作用对于优化至关重要.
研究的目的:
- 应用机器学习来预测细菌的存活率,以对抗化ZnO纳米颗粒.
- 分析兰兴奋剂对ZnO纳米粒子特性和抗菌活性的影响.
- 确定影响细菌生存的关键因素.
主要方法:
- 合成和表征胺ZnO纳米颗粒.
- 评估抗菌活性对抗阳性和阴性细菌.
- 实现机器学习算法 (MLP,KNN,GB,ERT) 用于生存预测.
主要成果:
- 兰兴奋剂没有显著改变ZnO的结构,形态或光学特性.
- 化ZnO纳米颗粒显示出不同的抗菌功效:60% (大肠杆菌),95% (P. aeruginosa),55% (S. aureus).
- 极端随机树模型实现了最高的预测准确度 (95.08%),超过了其他算法.
结论:
- 机器学习,特别是ERT,可以有效地预测细菌对抗La-doped ZnO纳米粒子的生存率.
- 发现兰含量和颗粒大小是细菌生存的无关预测因素.
- 这种数据驱动的方法加速了新型抗微生物纳米颗粒的开发.
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