在Cordiceps militaris中使用基于培养条件的机器学习预测Cordicepin内容的模型
Si Young Ha1, Hyeon Cheol Kim1, Jae-Kyung Yang1
1Department of Environmental Materials Science, Institute of Agriculture & Life Science, Gyeongsang National University, Jinju, Republic of Korea.
Journal of basic microbiology
|February 4, 2026
概括
这项研究开发了一种机器学习模型,用于预测Cordyceps militaris中的cordycepin含量. 基于种植参数,XGBoost模型准确地估计了cordycepin水平,有助于功能性食品和制药生产.
科学领域:
- 生物技术是生物技术.
- 菌类学 菌类学是指菌类学.
- 机器学习 机器学习
背景情况:
- 科迪塞宾是Cordyceps militaris中的一种生物活性化合物,具有重要的制药和功能性食品应用.
- 可变的生产需要高效的实时工艺控制,以获得一致的cordycepin产量.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测皮细胞的含量.
- 确定影响科迪塞生物合成的关键种植参数.
- 为实时预测创建一个用户友好的界面.
主要方法:
- 在实验数据上训练了XGBoost,随机森林和支持向量机算法.
- 使用环境和营养因素作为输入参数.
- 使用Tropsha的标准验证模型,并使用SHAP分析进行解释.
主要成果:
- XGBoost实现了最高的性能,Q2为0.9087和R2为0.9544,符合可靠性标准.
- SHAP分析显示,光的波长和碳/比为皮质生产的关键因素.
- 开发了一个图形用户界面 (GUI),用于立即预测cordycepin的含量.
结论:
- ML模型提供了一种可靠和可扩展的方法来优化皮质生产.
- 开发的GUI在食用真菌种植中促进了数据驱动的决策.
- 这种方法支持智能生物处理和精密发酵,用于制造生物活性化合物.
关键词:
科尔迪塞普斯军用菌 (Cordyceps militaris) 是一种植物.图形用户界面 (GUI) 是一个在XGBoost中使用.生物处理生物处理.科尔迪斯皮恩是什么意思机器学习是机器学习.更多相关视频
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