一个可解释的人工智能框架,用于设计基于合成致死性的抗癌组合疗法
Jing Wang1, Yuqi Wen2, Yixin Zhang2
1School of Medicine, Tsinghua University, Beijing, 100084, China.
Journal of advanced research
|December 3, 2023
概括
本研究介绍了KDDSL,这是一个可解释的AI框架,用于预测合成致命性 (SL) 相互作用. 通过将基因知识与人工智能结合起来,KDDSL有助于发现协同作用的癌症疗法,通过识别有效的药物组合来证明这一点.
科学领域:
- 计算生物学 计算生物学
- 在瘤学中使用人工智能
- 药物发现 药物发现 药物发现
背景情况:
- 合成致死性 (SL) 为开发用于癌症治疗的协同组合疗法提供了一个有前途的途径.
- 识别和机理理解SL相互作用对于推进癌症治疗设计至关重要.
- 目前用于SL预测的人工智能 (AI) 模型往往缺乏解释性,阻碍了机械洞察力.
研究的目的:
- 开发一个可解释的AI框架来预测合成致命性 (SL) 相互作用.
- 为了利用可解释的AI框架来设计基于SL的协同组合疗法.
- 增强对癌症中SL相互作用的机制性理解.
主要方法:
- 为SL预测提出了一个基于知识和数据的双驱动AI框架,命名为KDDSL.
- 与SL机制相关的综合基因知识,以指导模型构建.
- 开发了一种方法来识别驱动模型预测的关键基因知识.
主要成果:
- KDDSL在预测准确性和可解释性之间取得了有利的平衡.
- 通过实验和基于文献的证据验证的发现.
- 成功确定了有前途的药物组合,包括MDM2和CDK9抑制剂,具有显著的体外和体内抗癌作用.
结论:
- 基于KDDSL的框架显示了指导基于SL的组合疗法的设计的巨大潜力.
- 强调需要在生物医学中使用人工智能策略,将生物知识与预测模型整合起来.
- 强调可解释AI在发现治疗机制方面的价值.
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