灯塔:预测药物的副作用和治疗结果 通过桥接知识 边缘图形与语境语言模型
bioRxiv : the preprint server for biology
|February 9, 2026
概括
BEACON将生物医学知识图形转化为上下文语言,用于准确的预测. 这种可解释的框架增强了药物敏感性和药物相互作用预测,优于现有的深度学习方法.
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.
- 医学中的人工智能
背景情况:
- 生物医学知识图提供了大量的数据,但在准确预测的上下文解释方面遇到了困难.
- 当前的深度学习模型缺乏解释性,无法捕捉取决于上下文的生物功能.
- 现有的方法赋予固定实体表示,限制了它们对新情景的概括能力.
研究的目的:
- 引入BEACON (桥接知识边缘图与上下文语言模型),这是一个新的框架,用于对生物医学知识图进行上下文化.
- 为了提高来自生物医学知识图的预测的准确性和可解释性.
- 为了证明BEACON在药物敏感性和药物相互作用预测任务中的有效性.
主要方法:
- BEACON将知识图实体转换为令牌,关系转换为语法依赖,形成用于语言模型处理的"句子树".
- 可见度矩阵确保注意力机制遵守知识图形拓.
- 一个基于扰动的评估模块确定了预测的关键生物驱动因素.
主要成果:
- 在药物敏感性预测方面,BEACON实现了0.941 AUROC和Spearman ρ = 0.919,超过了现有的方法.
- 对于药物相互作用的预测,BEACON在TwoSIDES上达到0.964 AUROC,在FDA不良事件数据上达到0.84 AUROC.
- 对BEACON对acalabrutinib的预测进行了网络分析,确定CYP3A代谢是关键的相互作用机制,由生物数据验证.
结论:
- BEACON为知识图增强的生物医学预测提供了一种统一和可解释的方法.
- 该框架在各种生物医学应用中表现出卓越的性能和概括能力.
- BEACON的上下文化策略克服了生物信息学深度学习中的固定表示模型的局限性.
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