PBAC:一种基于途径的注意力卷积神经网络,用于预测临床药物治疗反应
Dexun Deng1,2,3, Xiaoqiang Xu1,2,4,5, Ting Cui1,2
1The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
我们开发了一个基于途径的注意力卷积神经网络 (PBAC) 来预测药物反应. 通过解释生物途径以改善治疗策略,PBAC增强了个性化医疗.
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.
- 机器学习在医学中的应用
背景情况:
- 个性化药物应用对于治疗复杂疾病至关重要.
- 神经网络为药物战略提供了希望,但缺乏可解释性.
- 了解生物通路是预测药物反应的关键.
研究的目的:
- 使用深度学习开发一个强大的,可解释的药物反应预测模型.
- 将生物路径信息集成到神经网络框架中.
- 提高预测患者对癌症治疗反应的准确性.
主要方法:
- 提出了基于路径的注意力卷积神经网络 (PBAC) 模型.
- PBAC集成了基因路径,注意力,卷积和完全连接的层.
- 验证了四种药物的化疗和免疫治疗数据集模型.
主要成果:
- 与传统方法相比,PBAC显示出更高的性能 (AUC=0.81,AUPRC=0.73).
- 注意力机制确定了参与药物反应的关键生物途径.
- 该模型为药物作用机制提供了可解释的见解.
结论:
- 通过利用生物通路数据,PBAC是预测药物反应的有效工具.
- 该模型为了解药物机制和指导治疗提供了有价值的解释性.
- 通过准确和可解释的药物反应预测,PBAC推进了个性化医疗.
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