GPDRP:使用图形变压器预测药物反应的多式联络框架
Yingke Yang1, Peiluan Li2,3
1School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, 471000, China.
BMC bioinformatics
|December 17, 2023
概括
这项研究引入了一种使用分子图形和基因通路的新型药物反应预测模型 (GPDRP). 通过利用图形转换器来增强药物表示和途径分析,GPDRP显著提高了预测准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 药物反应预测 (DRP) 在个性化医学中至关重要.
- 现有的DRP方法通常使用简化的药物表征 (字符串) 并忽视基因通路相互作用.
- 需要更复杂的方法来建模药物分子及其生物效应.
研究的目的:
- 开发一种多式联络深度学习模型,用于准确预测药物反应.
- 用分子图和细胞系通过基因通路活动来表示药物.
- 整合这些表示来提高预测性能.
主要方法:
- 提出了基于药物图和基因路径的药物反应预测方法 (GPDRP).
- 利用图形神经网络 (GNN) 与图形变压器进行分子图形处理.
- 使用深度神经网络进行基因通路活动分析.
- 使用完全连接的层进行集成预测.
主要成果:
- 在GPDRP中基于图形变压器的方法表现出卓越的性能.
- 在使用大量RNA测序数据的数百个癌症细胞系上,GPDRP的性能优于现有的模型.
- 该模型显示出强大的通用性和适用于新型药物细胞系对和异种移植.
结论:
- GPDRP提供了一种强大且可解释的药物反应预测方法.
- 纳入基因通路可以提高模型理解药物效应的能力.
- 这些发现支持在计算型个性化医学中使用分子图和途径数据.
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