迪莫斯:一种用于最佳分组的新型自动化方法. 纳米信息学的应用案例研究
Dimitra-Danai Varsou1, Haralambos Sarimveis1
1School of Chemical Engineering, National Technical University of Athens, 157 80, Athens, Greece.
Molecular informatics
|May 31, 2023
概括
我们开发了deimos,这是一种计算方法,用于最佳分组,以预测工程纳米材料 (ENM) 的毒性. 这种方法通过同时选择特征并为回归分析创建不同的组来改进预测建模.
科学领域:
- 计算毒理学计算毒理学
- 材料科学是一种材料科学.
- 预测建模的预测建模.
背景情况:
- 预测工程纳米材料 (ENM) 的毒性对于风险评估至关重要.
- 现有的方法往往缺乏同时优化复杂数据集的特征选择和组定义的能力.
- 阅读交叉方法需要强大的方法来分组相似的化学实体.
研究的目的:
- 介绍deimos,一种用于预测建模中最佳分组的新型计算方法.
- 将deimos应用于对ENM与毒性相关性质的横读预测.
- 为了比较deimos与标准回归技术的性能.
主要方法:
- 混合整数线性编程 (MILP) 问题的制定和解决方案,用于同时选择特征和定义组边界.
- 在每个已识别的群体内开发线性回归模型.
- 定义用于分配未经测试的ENM的组中心点.
- 在优化工作流中整合deimos以与多重线性回归 (MLR) 和LASSO进行比较.
主要成果:
- 德伊莫斯方法论在基准ENM数据集上表现出有效的应用.
- 性能比较显示deimos作为一个竞争性的预测建模方法.
- 该方法成功地同时执行特征选择,组定义和模型构建.
结论:
- 戴莫斯提供了一种先进的计算方法,用于优化预测毒理学中的分组.
- 该方法非常通用,可以在ENM之外应用到其他化学实体和属性预测.
- 德伊莫斯提高了阅读横向预测的准确性和效率.
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