利用多种来源来解决药物敏感性预测中药物基因组数据集之间的不一致性
Trisha Das1, Kritib Bhattarai2, Sivaraman Rajaganapathy3
1University of Illinois Urbana-Champaign, Champaign, Illinois, United States.
medRxiv : the preprint server for health sciences
|June 19, 2023
概括
联合学习 (FL) 通过整合各种药物基因组学数据集来改善药物反应预测. 这种方法提高了模型的概括性,克服了精确瘤学细胞系研究中发现的不一致性.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物基因组学数据集各不相同,导致药物反应预测不一致.
- 瘤间异质性和实验因素限制了当前模型的概括性.
- 准确的药物反应预测对于推进精确瘤学的发展至关重要.
研究的目的:
- 开发使用联合学习 (FL) 的计算模型,以改进药物反应预测.
- 为了应对药物基因组学数据分析中有限的概括性的挑战.
- 为了利用多个数据集进行强大和通用的预测模型.
主要方法:
- 实施了用于药物反应预测的联合学习 (FL) 模型.
- 使用了三个不同的药物基因组学数据集:CCLE,GDSC2和gCSI.
- 在各种基于细胞系的数据库中评估模型性能.
主要成果:
- 拟议的FL模型与基线和传统FL方法相比,显示出优异的预测性能.
- 实验测试证实了该模型在各种数据集中的有效性.
- 这种方法成功地利用了多个数据源来增强概括性.
结论:
- 联合学习 (FL) 提供了一种强大的方法来克服药物基因组学数据中的不一致性.
- 开发的模型显示了在精确瘤学中推进药物反应预测的巨大潜力.
- 这项研究强调了一般化模型对现实世界临床应用的价值.
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