使用人工神经网络建模非线性不可逆的生物化学反应的智能框架
Hazrat Bilal1, Rehan Ali Shah1, Hijaz Ahmad2,3,4,5
1Department of Basic Science and Islamiate, University of Engineering and Technology Peshawar, Peshawar, Pakistan.
Scientific reports
|August 4, 2025
概括
本研究介绍了一种人工神经网络 (ANN) 框架,用于建模复杂的生化反应. 与其他方法相比,反向传播莱文伯格-马奎特算法表现出卓越的准确性和速度.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 非线性不可逆的生化反应 (NIBR) 是生物过程的基础.
- 准确地建模NIBR对于理解细胞机制至关重要.
- 现有的模型可能会面临复杂的反应动力学的挑战.
研究的目的:
- 开发一个智能计算框架来建模NIBR.
- 利用人工神经网络 (ANN) 来模拟生化反应动态.
- 为了比较不同ANN训练算法的NIBR建模的性能.
主要方法:
- 使用扩展的迈凯利斯-门登运动方案和非线性普通微分方程 (ODEs) 建模的生物化学反应.
- 通过Runge-Kutta第四顺序 (RK4) 方法生成的数据集.
- 使用反向传播莱文伯格-马奎特 (BLM) 算法训练的多层前ANN,与贝叶斯规范化 (BR) 和缩放结合梯度 (SCG) 相比.
- 在六个动力场景中,模型验证具有变速常量.
主要成果:
- 在准确性,融合速度和稳定性方面,BLM-ANN模型显著超过了BR和SCG.
- 平均平方误差 (MSE) 低至[公式:参见文本],由BLM-ANN模型实现.
- 通过回归图表证实了BLM-ANN预测和RK4解决方案之间的高相关性.
- 错误分布验证了模型的预测能力.
结论:
- 拟议的BLM-ANN框架为建模NIBR提供了一个高度准确和可靠的方法.
- 该框架在各种动力学配置文件中显示出出色的概括能力.
- 这种方法为生物化学系统分析提供了强大的计算工具.
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