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在Aureobasidium pullulans CGMCCNO.705555的生产中应用可解释的机器学习

Shiwei Chen¹, Wenmin Li¹, Xiaowen Zhao¹

¹State Key Laboratory of Bio-based Fiber Materials, Tianjin University of Science and Technology, Tianjin 300457, P.R. China; Key Laboratory of Industrial Fermentation Microbiology, Tianjin University of Science and Technology, Ministry of Education, Tianjin 300457, China; Tianjin Engineering Research Center of Microbial Metabolism and Fermentation Process Control, School of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, China.

International journal of biological macromolecules

|March 26, 2025

概括

可解释的机器学习模型,包括CatBoost和XGBoost,优化了Pullulan生物发酵. 酵母提取物是关键,NSGA-III算法确定了增强生物质和pullulan生产的最佳条件.

科学领域:

生物技术是生物技术.
发酵科学发酵科学
机器学习应用机器学习应用

背景情况:

普鲁伦生物发酵对于生产这种多糖至关重要.
可解释的机器学习 (XAI) 在复杂的生物过程中提供了透明度.
了解介质成分效应对于优化普鲁兰产量和质量至关重要.

研究的目的:

为了比较6个机器学习模型对Pullulan生物发酵的预测性能.
确定影响生物质,普卢兰生产和分子重量的关键介质成分.
使用多目标优化算法来确定最佳的发酵条件.

主要方法:

评估了六种机器学习模型,包括分类提升 (CatBoost) 和极端梯度提升 (XGBoost).
用特征重要性和夏普利添加式解释 (SHAP) 来进行变量分析.
非主导排序基因算法III (NSGA-III) 用于多目标优化.

主要成果:

CatBoost最好地预测了生物质和普鲁兰分子量;XGBoost在普鲁兰产量预测方面表现出色.
酵母提取物是所有目标中最重要的因素.
通过NSGA-III确定的最佳条件产生了275.08%的集成优化率.

结论:

可解释的机器学习模型可以有效地预测和优化Pullulan生物发酵.
酵母提取物,NaCl和初始pH值是调节pullulan特征的关键因素.
这项研究表明,NSGA-III的成功应用是为了实现多目标的pullulan生产优化.

关键词:

可解释的机器学习这是NSGA-III.普卢卢兰 (Pullulan) 的意思是拉拉兰.这就是 SHAP SHAP 的意思.

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