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Updated: May 13, 2025

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使用机器学习对鱼类中有机化学物质的生物度因子进行预测建模和解释性分析

Xuanzhi Dong1, Zhenpeng Xu2, Hongxia Zhao2

  • 1Key Laboratory of Facility Fisheries (Ministry of Education), School of Marine Science, Technology and Environment, Dalian Ocean University, Dalian, 116024, China.

Environmental pollution (Barking, Essex : 1987)
|May 10, 2025
PubMed
概括

一个新的机器学习 (ML) 模型使用分子特征预测化学生物积累潜力. 随机森林 (RF) 模型有效地识别了具有高生物度因子 (BCF) 值的化学物质,有助于环境风险评估.

关键词:
生物度因子是生物度因子.机器学习是机器学习.分子描述器分子描述器量化结构与活动的关系.这就是 SHAP SHAP 的意思.

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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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