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快速的多药副作用预测使用张量因子化
Oliver Lloyd1, Yi Liu1, Tom R Gaunt1
1MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, United Kingdom.
优化的张量分解模型准确地预测药物组合不良反应. 简单E模型有效地实现了最先进的结果,为多药副作用预测提供了更快的替代方案.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
背景情况:
- 药物组合的不良反应在医学中越来越令人担忧.
- 实验室方法不足以预测这些组合效应.
- 计算方法,包括张量分解 (TF),已经显示出潜力,但需要优化.
研究的目的:
- 调查优化张量因子化模型对多药副作用预测的有效性.
- 与现有方法相比,评估TF模型的性能和效率.
- 确定TF模型中单药药房数据的最佳整合.
主要方法:
- 利用张量分解 (TF) 模型,特别是SimplE模型,来预测多药副作用.
- 在基于图表的方法中,集成的单药数据作为自循环边缘.
- 在NVIDIA GPU上使用PyTorch 1.7.1的Python 3.8.12训练模型.
主要成果:
- 简单的TF模型实现了最先进的性能,AUC ROC为0.978,AUC PR为0.971,AP@50为1.000在963种副作用中.
- 该模型在两个培训时代 (大约2年) 内达到其峰值性能的98.3%. 4分钟),显示出显著的速度优势.
- 将单药药房数据集成为自循环边缘产生了比使用它嵌入初始化略有更好的结果.
结论:
- 优化的张量分解模型,如SimplE,对于预测多药副作用非常有效.
- 与现有方法相比,这些模型提供了一个计算效率高,准确的解决方案.
- 该研究强调了TF在促进药物安全和个性化医疗方面的潜力.
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