通过在生物医学文献中嵌入文本来对药物相互作用进行可解释的预测
1Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju, 61186, South Korea.
Computers in biology and medicine
|December 3, 2024
概括
本研究引入了一种深度学习模型,用于预测药物相互作用 (DDI) 和其类型,通过分析生物医学文献的潜在风险来提高患者的安全性和降低医疗保健成本.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 医学中的人工智能
背景情况:
- 多药性,即多种药物的使用,对于复杂的疾病至关重要,但有药物相互作用 (DDI) 和药物不良反应 (ADR) 的风险.
- 准确预测DDI及其机制对于安全有效的药物治疗至关重要.
研究的目的:
- 开发和评估基于注意力的层次深度学习模型,用于预测DDI及其特定类型.
- 利用生物医学文献来提高DDI预测准确性和可解释性.
主要方法:
- 一个由两个组成部分组成的模型:使用句子和序列方法嵌入药物 (预训练的生物医学语言模型,双向LSTM与层次关注).
- 使用在药物对序列嵌入矢量上的深度神经网络进行DDI预测.
- 通过注意力机制和药物相似性分析来解释模型.
主要成果:
- 在164种DDI类型中实现了高性能:精度 (0.85-0.90),AUROC (0.98-0.99),AUPR (0.63-0.95).
- 与基线相比,AUROC高达11%,AUPR高达8%,表现出显著的改善.
- 模型解释显示,除了简单的药物相似性之外,还考虑了其他因素.
结论:
- 拟议的深度学习模型从生物医学文献中准确预测了DDI及其类型.
- 该模型的可解释性有助于理解超越药物相似性的相互作用机制.
- 通过精确的DDI预测,研究结果支持预防医疗事故和降低医疗保健成本.
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