可解释的深度学习架构,以提高药物反应预测性能:神话还是现实?
Yihui Li1, David Earl Hostallero1,2, Amin Emad1,2,3
1Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
Bioinformatics (Oxford, England)
|June 16, 2023
概括
评估了用于药物反应预测 (DRP) 的可解释深度学习模型,其中包括信号通路. 显式路径集成的表现更差,黑盒模型往往达到更高的准确性和通用性.
科学领域:
- 生物医学信息学是生物医学信息学.
- 计算生物学是一种计算生物学.
- 机器学习在药物发现中的作用
背景情况:
- 可解释的深度学习 (DL) 模型对于生物医学领域的药物反应预测 (DRP) 是至关重要的.
- 最近,DRP出现了集成生物信号通路的DL模型,旨在提高可解释性.
- 路径集成对DRP准确性和通用性的影响仍然是一个开放的问题.
研究的目的:
- 系统地评估用于DRP的最先进的可解释DL模型.
- 为了比较显式与隐式结合路径信息的模型.
- 在独立数据集上评估模型性能和概括性.
主要方法:
- 使用三个路径集合对四个可解释的DL模型进行了全面评估.
- 对未见样本的预测准确性和对独立数据集的概括性进行评估.
- 与黑盒模型 (多层感知子) 和基线 (随机森林) 的比较.
主要成果:
- 显式整合路径信息的模型表现比含有隐式整合的模型要差.
- 黑盒多层感知子模型通常实现了最佳性能.
- 随机森林的基线性能与可解释模型可比;随机路径产生了类似的结果.
- 当应用到独立数据集时,模型性能显著下降.
结论:
- 在DL模型中明确的途径集成不一定会提高药物反应预测的准确性.
- 用适当的基线进行系统评估对于评估新的DL模型至关重要.
- 黑盒模型和更简单的方法可以在DRP中提供竞争力或优异的性能.
- 对独立数据集的概括性仍然是当前可解释的DL模型面临的重大挑战.
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