神经相互作用可解释的人工智能预测跨癌症的药物反应
Philipp Keyl1,2,3,4, Julius Keyl5,6, Andreas Mock2,7
1Institute of Pathology, Charité-Universitätsmedizin Berlin, 10117 Berlin, Germany.
NAR cancer
|September 8, 2025
概括
这项研究介绍了NeurixAI,一种AI框架,使用基因表达数据预测癌症药物反应. 它识别了个性化的治疗方法,并重新定位了药物的用途,推进了精确瘤学.
科学领域:
- 计算生物学 计算生物学
- 人工智能在医学中的应用
- 癌症基因组学 癌症基因组学
背景情况:
- 癌症治疗的有效性在患者之间有很大差异.
- 目前的疗法通常依赖于人口平均值,而不是单个分子概况.
- 可操作的突变指导了一些治疗选择,但大多数疗法缺乏个性化.
研究的目的:
- 开发一个可扩展的深度学习框架 (NeurixAI) 用于建模药物基因相互作用和预测癌症药物反应.
- 识别与治疗结果相关的转录组模式.
- 利用可解释的人工智能 (xAI) 发现药物反应和耐药性的机制.
主要方法:
- 在大量药物干扰实验 (546,646) 和瘤分子概况 (476) 的数据集上训练了一种深度学习框架 (NeurixAI).
- 模拟了与药物敏感性相关的药物基因相互作用和转录组模式.
- 应用可解释AI (xAI) 来识别影响个体瘤反应的关键基因和机制.
主要成果:
- 在未见的瘤样本中,NeurixAI准确预测了272种向药物和30种化疗药物的治疗反应 (斯皮尔曼的rho>0.2).
- 该框架在外部验证集上表现出高性能.
- 确定了160种具有抗癌潜力的重用非癌症药物,并阐明了已知的/新药耐药性机制.
结论:
- 将转录学与可解释AI (xAI) 集成为优化个性化癌症治疗提供了一种强大的方法.
- NeurixAI可以通过预测药物疗效和识别新的治疗点来提高精确瘤学.
- 该框架促进了药物的重新用途,并提供了对个体瘤生物学的见解,以改善治疗策略.
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