一种机器学习方法,用于预测细胞机械应力,以应对化学扰动
VigneshAravind SubramanianBalachandar1, Md Mydul Islam2, R L Steward3
1Department of Mechanical and Aerospace Engineering, College of Engineering, University of Central Florida, Orlando, Florida; Department of Cell Biology, University of Virginia, Charlottesville, Virginia.
Biophysical journal
|July 27, 2023
概括
机器学习模型预测化学化合物如何影响细胞机械应力. 一个离散窗口预测器准确地预测细胞间的应力和引力,有助于治疗发现.
科学领域:
- 生物物理学的生物物理.
- 计算生物学 计算生物学
- 细胞力学 细胞力学
背景情况:
- 细胞细胞和细胞基质水平上的细胞机械应力在生理和病理过程中至关重要.
- 化学化合物对这些机械应力的影响尚不清楚,这限制了治疗的发展.
研究的目的:
- 开发机器学习模型来预测细胞对化学干扰的剂量依赖机械反应.
- 克服了解化学影响细胞机械应力方面的障碍.
主要方法:
- 实现两个预测器:单层边界预测器和离散窗口预测器.
- 使用逐步线性回归和二次支持向量机器模型.
- 训练模型与实验引,细胞间应激数据,和细胞形态从不同的药物度 (0.2,2μg/mL).
主要成果:
- 在有限的数据 (4个样本,292张图像) 上训练的离散窗口预测器显示出高的预测准确性.
- 方位支向量机和渐进线性回归模型对未见药物度 (0,1μg/mL) 进行了有效预测,分别预测了细胞间应力和引力.
结论:
- 机器学习,特别是离散窗口预测器,可以准确地预测细胞对化学刺激的机械反应.
- 这种方法通过阐明化学化合物对细胞机制的影响,促进了新疗法的发现.
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