基于机器学习的预测药物反应在缺血再输动动物模型的药物反应
Asmaa Mohamed Abd ElGwad1, Ibrahim Youssef2, Abdelrahman Khaled3
1Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt. Asmaamhd@med.asu.edu.eg.
Scientific reports
|December 2, 2025
概括
机器学习模型预测心肌缺血-再输液 (MI/R) 损伤的药物反应. k-最近邻居 (kNN) 模型显示出高准确性,识别了个性化治疗策略的关键分子和生化标志物.
科学领域:
- 心血管研究研究心血管研究
- 计算生物学 计算生物学
- 药物基因组学 药物基因组学
背景情况:
- 心肌缺血-反 (MI/R) 损伤会导致不可逆转的心脏损伤,治疗选择有限.
- 迫切需要对MI/R损伤进行有效的干预,以减少死亡率.
- 了解MI / R损伤的发病因子对于开发新疗法至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测MI/R损伤中的药物反应.
- 确定与治疗疗效相关的关键分子和生化标志物.
- 探索个性化治疗在治疗MI/R损伤方面的潜力.
主要方法:
- 使用了监督机器学习模型 (逻辑回归,SVM,随机森林,神经网络,kNN).
- 顺序前选择 (SFS) 用于特征选择.
- 模型性能使用精度,准确性,回忆,特异性和MCC进行了评估.
主要成果:
- k-最近邻居 (kNN) 模型实现了最高的精度 (0.9156 ± 0.0242) 和平均AUC为0.90.
- 一个整合分子和生物化学标记的双层框架增强了模型的稳定性和可解释性.
- 药物反应的重要预测因素包括SOX5 (分子) 和dP/dtmax,cTnT (生物化学).
结论:
- 机器学习,特别是kNN,可以有效地预测MI/R损伤中的药物反应.
- 使用分子和生物化学标记物的综合方法可以提高预测准确性和生物相关性.
- 这一策略有望推动个性化医学的发展,治疗心肌缺血症.
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