MEMMAL:一个工具来扩展大规模的机械模型与机器学习协会和大数据集的工具
Cemal Erdem1, Marc R Birtwistle1,2
1Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, United States.
Frontiers in systems biology
|January 25, 2024
概括
我们介绍了一种结合机器学习和机械模型来预测药物反应的新框架. 这种方法将数据驱动的见解与已建立的生物知识相结合,用于增强精确医学应用.
科学领域:
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 计算模型对于药物发现和精准医学至关重要.
- 机械学和机器学习模型很少结合在一起,这限制了它们的预测能力.
- 整合这些方法可以发现新的生物学见解,并提高模型的准确性.
研究的目的:
- 开发一个混合计算框架 (MEMMAL),将机械模型与机器学习相结合.
- 利用奥米克数据来预测新的基因-蛋白相互作用,并将其纳入机械模型.
- 为了增强细胞行为和药物反应机制的表现.
主要方法:
- 开发了机械模型与机器学习 (MEMMAL) 框架.
- 利用omics数据集来训练机器学习模型来预测基因-蛋白质相互作用.
- 将预测的相互作用集成到细胞增殖和细胞死亡的大规模机械模型中.
- 使用NIH LINCS联盟MCF10A数据集和对免疫疗法的反应验证了该模型.
主要成果:
- MEMMAL框架成功地将数据驱动的预测集成到一个机械模型中.
- 改进的模型更好地总结了细胞增殖和细胞死亡的实验数据.
- 该模型能够描述细胞对检查点抑制剂免疫疗法的反应.
结论:
- 结合机器学习和机械建模,为构建全面的细胞模型提供了一种强大的方法.
- 这种混合战略可以加速药物发现,并推进精准医学.
- MEMMAL框架作为多尺度建模和理解复杂生物系统的模板.
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