使用全药基因再测序的药物体扫描,加上用于临床药物基因组学的深度计算分析和机器学习
Alireza Tafazoli1,2, John Mikros3, Faeze Khaghani4
1Department of Analysis and Bioanalysis of Medicines, Faculty of Pharmacy With the Division of Laboratory Medicine, Medical University of Bialystok, 15-089, Białystok, Poland.
Human genomics
|July 14, 2023
概括
这项研究引入了新的计算和人工智能方法,以功能性地评估整个外体测序数据中的未知药物变异,识别影响药物反应的关键变异. 这些方法通过改善对药物不良反应的预测来增强个性化医疗.
科学领域:
- 基因组学和生物信息学
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
背景情况:
- 整体外基因组测序 (WES) 产生了大量的数据,包括具有未知的功能影响的药物变异.
- 现有的生物信息学工具主要集中在已知的变异上,在与药物相关的基因中对新型标记物的功能评估中留下了一个空白.
研究的目的:
- 用新型的深度计算分析和人工智能 (AI) 来识别和功能评估与药物相关基因内的药物变异.
- 探索机器学习 (ML) 模型对基于药物变量预测药物不良反应的实用性.
主要方法:
- 涉及8个生物信息算法和23个工具的深度计算分析应用于来自100个个人的WES数据.
- 随机森林 (RF) 分类器被用作一种ML方法来进行变异评估和预测.
- 对高影响变异进行了蛋白质建模和基因型-表型相关性.
主要成果:
- 在RYR1,POLG,ANXA11,CCNH和CDH23基因中发现了五种有害的药物变异,这些变异对与药物相关的表型产生了重大影响.
- 一个射频模型在使用内部交叉验证的175个变体来预测药物不良反应时取得了高准确性 (AUC0.97,准确度0.98).
- 外部交叉验证显示性能较低 (AUC0.54,精度0.95),突出了有限数据的潜在假阳性.
结论:
- 需要先进的计算工具来功能性地预测大和小数据集中的药物变量.
- 开发的方法为选择适当的计算方法提供了新的见解,用于使用高通量测序数据进行个性化药物治疗.
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