使用因果启发的神经网络对治疗干扰的组合预测
Guadalupe Gonzalez1,2,3, Xiang Lin4, Isuru Herath5,6
1Imperial College London, London, UK.
bioRxiv : the preprint server for biology
|January 23, 2024
概括
PDGrapher是一种新型图形神经网络 (GNN),直接预测治疗点的组合以逆转疾病表型. 这种方法通过比传统方法更快地识别有效的干扰来加速药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 网络药理学 网络药理学
- 机器学习用于药物发现.
背景情况:
- 以表型为驱动的方法通过分析细胞状态差异来识别疾病调节化合物.
- 针对性干扰,如药物或基因干预,旨在将疾病状态转移到更健康的形状.
- 当前的方法经常间接推断扰动,需要大量的计算.
研究的目的:
- 介绍PDGrapher,一个因果启发的图形神经网络 (GNN).
- 开发一种方法,直接预测用于逆转疾病表型的组合性扰素.
- 为了加快治疗标的识别在表型驱动的药物发现.
主要方法:
- PDGrapher将疾病细胞状态嵌入到生物网络中 (基因调节或蛋白质与蛋白质相互作用).
- 它学习细胞状态的潜在表示,以确定最佳的组合扰动.
- 该模型解决了相反的问题:预测预期的表型结果的扰动因子.
主要成果:
- 与化学扰动实验中的现有方法相比,PDGrapher在多达13.33%的样本中发现了有效的扰动剂.
- 为了对治疗目标进行分类,实现了更高的正常化折扣累积收益 (高达0.12).
- 在基因扰乱数据集上表现出竞争性表现,并将训练加速高达25倍.
结论:
- PDGrapher提供了一个直接预测范式,克服了传统的表型驱动方法的计算强度.
- 在GNN加快治疗干扰的识别.
- 这一进步促进了更快,更有效的表型驱动药物发现.
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