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Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
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A Method to Assess Bacteriocin Effects on the Gut Microbiota of Mice
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通过基于交叉验证和基于超图的特征评估方法进行细菌素预测.

Suraiya Akhter1,2,3, John H Miller2

  • 1School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, United States.

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概括
此摘要是机器生成的。

这项研究开发了一个基于网络的XGBoost模型来预测细菌素,这是抗生素耐药性的潜在解决方案. 基于超图的特征评估方法实现了99.11%的准确性,有助于新药的开发.

关键词:
沙普利添加剂的解释抗微生物类的抗微生物.抗微生物耐药性 抗微生物耐药性细菌素预测的预测功能选择 功能选择机器学习是机器学习.网络应用程序 网络应用程序

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科学领域:

  • * 计算生物学 计算生物学
  • * 生物信息学是一门学科.
  • * 机器学习在药物发现中的作用

背景情况:

  • *抗生素耐药性是一个日益增长的全球健康威胁.
  • * 细菌素显示出作为向抗菌剂的前景.
  • *需要预测模型来加速细菌的发现和药物开发.

研究的目的:

  • * 开发和验证基于网络的计算模型,用于预测细菌菌.
  • *比较特征选择方法,包括交叉验证特征选择 (CVFS) 和基于超图的特征评估 (HFE).
  • * 确定影响细菌素活性的关键蛋白质特征.

主要方法:

  • *使用蛋白质序列数据构建XGBoost机器学习模型.
  • *使用CVFS和HFE技术进行特征选择.
  • *使用夏普利添加式扩展 (SHAP) 分析特征重要性.
  • * 开发一个公开可访问的网络应用程序,用于细菌素预测.

主要成果:

  • *基于HFE的XGBoost模型在测试数据上实现了99.11%的准确性和0.9974的AUC.
  • *HFE方法的性能优于CVFS方法,并且与现有的方法相匹配.
  • *关键的预测特征包括埋藏残留物的溶剂可访问性和氨酸成分.

结论:

  • * 计算模型,特别是使用HFE的模型,可以有效地预测细菌菌.
  • *开发的网络应用程序促进了细菌的发现,并有助于抗生素药物的开发.
  • * 了解特征贡献可以提高预测模型的解释性.