提高药物前节律失常风险评估的in silico分类器的性能和可解释性,使用多生物标志物方法与排名算法
Ali Ikhsanul Qauli1, Nurul Qashri Mahardika T2, Ulfa Latifa Hanum2
1Kumoh National Institute of Technology, IT convergence engineering, Gumi 39177, Republic of Korea; Universitas Airlangga, Faculty of Advanced Technology and Multidiscipline, Department of Engineering, Surabaya, Indonesia.
Computer methods and programs in biomedicine
|January 23, 2025
概括
改善药物前节律失常风险评估需要结合多个生物标志物. 我们的增强模型使用多个生理标记来更好地预测和解释,为单个生物标志物方法提供了切实可行的替代方案.
科学领域:
- 计算毒理学计算毒理学
- 药理学 药理学是指药理学的学科.
- 机器学习在药物安全方面的应用
背景情况:
- 在Silico药物评估系统预测了前节律失常的风险.
- 目前的领先系统使用单个生物标志物,如qNet,提供可解释性.
- 先进的模型使用多个生物标志物来更好地预测,但缺乏直觉.
研究的目的:
- 开发一种使用多种生物标志物进行前节律失常风险评估的方法.
- 为了保持可解释性,同时提高预测能力.
- 为药物安全性评估提供实用替代方案.
主要方法:
- 增强的顺序逻辑回归 (OLR) 与额外的生理生物标志物.
- 引入了通用torsade度量得分 (TMS) 为多生物标志物可解释性.
- 采用多标准决策分析排名算法进行分类器评估.
主要成果:
- 与单个qNet相比,多生物标志物方法显示出更高的性能.
- 一些没有qNet的OLR模型的表现优于包含qNet的OLR模型.
- 单个表现不佳的生物标志物可以在组合中得到改善.
结论:
- 拟议的方法有效地利用多种生物标志物进行前节律失常风险评估.
- 该TMS为多生物标志物OLR模型提供了简单的解释性.
- 为药物安全性评估提供实用和可解释的替代方案.
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