通过坐标不变机器学习数据驱动的细菌化学迁移的发现
Yorgos M Psarellis1, Seungjoon Lee2, Tapomoy Bhattacharjee3
1Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
BMC bioinformatics
|October 25, 2024
概括
机器学习模型现在可以从实验数据中推断细菌化学反应,从而绕过了对完整现场测量的需求. 这种方法有助于了解细菌的运动,并预测密度概况.
科学领域:
- 计算生物学 计算生物学
- 生物物理学的生物物理.
- 机器学习 机器学习
背景情况:
- 传统上,研究大肠杆菌化学反应涉及实验或部分微分方程 (PDEs).
- 将实验数据与PDE联系起来是具有挑战性的,因为缺乏有关基础领域和条件的信息.
- 本研究提出了机器学习 (ML) 方法,灵感来自嵌入定理,以克服这些挑战.
研究的目的:
- 开发和验证ML方法来推断化疗部分微分方程 (PDEs).
- 证明ML模型能够从各种数据源中学习细菌运动性的能力.
- 调查细菌密度数据是否可以弥补未测量的化学营养素场.
主要方法:
- 利用惠特尼和塔肯斯启发的机器学习算法嵌入定理.
- 经过验证的ML模型,使用已建立的连续模型中的模拟数据.
- 应用ML以从实验细菌密度数据推断化疗PDEs.
主要成果:
- ML方法成功地从模拟和实验数据中推断出潜在的PDEs.
- 开发了"黑盒子"和"灰盒子"模型来表示化学性PDE.
- 表明,有限的细菌密度史可以推断化学营养素度,弥补缺失的测量.
结论:
- 数据驱动的PDEs是宏观化疗模拟的有价值工具.
- 这些模型可以从各种数据类型和可信度中学习,包括计算和实验来源.
- 推断PDEs可以预测细菌密度,近似参数,并估计未测量的领域,如化学营养素度.
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