XMR:一种可解释的多式联络神经网络,用于药物反应预测
Zihao Wang1, Yun Zhou2, Yu Zhang3
1Department of Computer Science, Indiana University Bloomington, Bloomington, IN, United States.
Frontiers in bioinformatics
|August 21, 2023
概括
这项研究介绍了一种可解释的多式联络神经网络 (XMR),用于预测癌症药物反应. 该XMR模型整合了基因组和药物结构数据,优于现有方法,并为个性化癌症治疗提供了生物学见解.
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.
- 机器学习在瘤学中
背景情况:
- 大规模的临床前癌症药物反应数据库使药物发现成为可能.
- 深度学习模型擅长预测癌症药物反应,但缺乏可解释性.
- 现有的可解释模型在临床应用中表现有限.
研究的目的:
- 开发一种可解释的多式联络神经网络 (XMR),用于准确和可解释的癌症药物反应预测.
- 整合基因组和药物结构特征,以增强预测建模.
- 为癌症中预测的药物反应提供生物学理由.
主要方法:
- 开发了XMR模型,一个多模式的神经网络,用于基因组特征的可见神经网络 (VNN) 和药物结构的图形神经网络 (GNN).
- 通过多式联接层集成VNN和GNN来建模药物反应.
- 利用 Reactome Pathway 数据库中的路径层次结构作为 VNN 架构,用于预测三阴性乳腺癌药物反应.
- 应用了修剪方法,以提高模型的可解释性.
主要成果:
- 与最先进的可解释深度学习模型相比,XMR模型显示出更高的预测性能.
- 该模型成功提供了解释三阴性乳腺癌药物反应的生物学见解.
- VNN和GNN的组合有效地捕获了关键的基因组和分子特征.
结论:
- XMR模型为癌症药物反应提供了预测准确性和生物解释性的平衡.
- 这种方法有望通过基于患者特定数据识别有效药物来推进个性化癌症治疗.
- 基因组和结构数据的多式融合增强了对药物与癌症相互作用的理解.
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