基于转移学习方法的小型样本对Gunqile-7的毒性预测和分类
Hongkai Zhao1, Sen Qiu1, Meirong Bai2
1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.
Computers in biology and medicine
|March 26, 2024
概括
这项研究引入了一种新的方法,使用数据增强和转移学习来预测蒙古药 Gunqile-7.的毒性. 该方法显著提高了对小数据集的预测准确性,这对于药物安全性评估至关重要.
科学领域:
- 药理学和计算机毒理学
- 蒙古传统医学 蒙古传统医学
背景情况:
- 药物诱导的疾病是阳性疾病的一个重要方面.
- 评估像Gunqile-7这样的传统药物的毒性对于患者的安全至关重要.
- 传统的药物毒性动物试验是昂贵的,并且产生很小的数据集.
研究的目的:
- 开发一种高效的计算方法来预测Gunqile-7的毒性.
- 用先进的机器学习克服药理试验中小样本大小的局限性.
- 加强蒙古传统医学的安全评估.
主要方法:
- 采用数据增强来扩大Gunqile-7毒性的有限数据集.
- 利用转移学习与一维卷积神经网络进行模型训练.
- 应用支持向量机器-递归特征消除,以实现有效的特征选择.
主要成果:
- 拟议的方法在预测Gunqile-7毒性方面表现出更高的准确性.
- 与没有转移学习的模型相比,准确度提高了多达9个百分点.
- 通过数据增强,成功地减少了所需训练样本的数量.
结论:
- 数据增强和转移学习的结合对于使用小数据集进行毒性预测是有效的.
- 这种方法提供了一个具有成本效益和准确的替代品,用于药物安全的传统动物试验.
- 该研究验证了计算方法在评估传统药物的实用性.
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