SAFER:以注意力为基础的子超图神经网络,用于预测对剂量组合的有效反应
Yi-Ching Tang1, Rongbin Li2, Jing Tang3,4
1Department of Health Data Science and Artificial Intelligence, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, USA.
SAFER是一种新的AI模型,通过考虑剂量效应和动态生物网络来预测药物组合协同作用. 这种方法通过确定针对个体患者量身定制的安全有效的药物组合来增强个性化癌症治疗.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 人工智能在医学中的应用
背景情况:
- 药物组合在癌症治疗中提供了显著的好处,但会增加毒性.
- 目前用于预测药物协同作用的AI模型往往忽略了关键的剂量信息和动态的生物相互作用.
研究的目的:
- 开发一个先进的AI模型,准确预测药物组合协同作用,考虑剂量特定效应和动态生物网络.
- 克服现有模型的局限性,这些模型忽略了剂量和静态相互作用数据.
主要方法:
- 介绍SAFER (亚超图基于注意力的图形模型),该模型包含复杂的生物关系和剂量依赖的效应.
- 利用特定主体的网络和注意力机制来建模动态交互.
主要成果:
- 与基准和独立数据集的现有模型相比,SAFER表现优越.
- 分析揭示了关键的生物途径和基因,如JAK-STAT信号传递,涉及肺癌和纤维化.
结论:
- SAFER提供了一个可解释的框架,用于识别药物反应信号和理解剂量级组合效应.
- 该模型促进了个性化医疗,使得基于个体分子形状的有效和安全药物组合能够优先考虑.
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