药物反应预测中的数据不平衡:深度学习环境中的多目标优化方法
Oleksandr Narykov1, Yitan Zhu1, Thomas Brettin1
1Computing, Environment and Life Sciences, Argonne National Laboratory, 9700 S Cass Ave, Lemont, IL 60439, United States.
这项研究引入了一种新的多目标优化方法,以改善抗癌药物反应预测 (DRP) 模型. 通过解决数据不平衡,该方法提高了针对个性化医学和药物发现的深度学习模型性能.
科学领域:
- 计算生物学是一种计算生物学.
- 机器学习在瘤学中
- 药物的发现和开发.
背景情况:
- 药物反应预测 (DRP) 将患者遗传与药物有效性联系起来,这对于个性化癌症治疗至关重要.
- 与其他AI领域相比,抗癌DRP由于广泛的致病机制和有限的数据深度而复杂.
- 现有的DRP模型与数据不平衡作斗争,阻碍了概括性和临床应用.
研究的目的:
- 制定解决DRP数据集数据不平衡的策略.
- 提高基于深度学习的DRP模型的通用性和性能.
- 将DRP重新定义为跨多种药物的多目标优化问题.
主要方法:
- 实施了由损失 (MOORLE) 损失函数规范的多目标优化.
- 将MOORLE损失函数集成到一个深度学习模型架构中.
- 评估了抗癌药物查数据集的方法.
主要成果:
- 通过解决数据不平衡,证明了DRP模型的改进性能.
- 多目标优化策略增强了模型的概括性.
- 拟议的方法显示出用于推进药物发现和个性化医学的实用性.
结论:
- 摩尔尔方法有效地解决了DRP中的数据不平衡问题.
- 这一策略增强了针对抗癌药物反应预测的深度学习模型性能.
- 这项工作为改善药物发现和医疗保健结果提供了一条途径.
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