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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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相关实验视频

Updated: Jul 11, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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机器学习用于皮肤透性预测:随机森林和XG促进回归.

Kevin Ita1, Joyce Prinze1

  • 1College of Pharmacy, Touro University, Vallejo, CA, USA.

Journal of drug targeting
|November 14, 2023
PubMed
概括

机器学习模型使用亚伯拉罕描述符预测皮肤透性 (Kp). 随机森林和XG Boost有效估计了175种化合物的Kp,有助于药物输送研究.

科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算化学计算化学
  • 材料科学 材料科学 材料科学

背景情况:

  • 机器学习 (ML) 模型对于估计药物输送中的皮肤透率 (Kp) 是至关重要的.
  • 亚伯拉罕的线性自由能量关系 (LFER) 是Kp预测的一个关键方法.
  • 有175种含有Kp和亚伯拉罕溶解物描述物的化合物的数据集.

研究的目的:

  • 使用随机森林和XG Boost回归来预测皮肤透率 (Kp).
  • 为了利用公开可用的数据集进行Kp估计.
  • 探索ML在预测通过皮肤递送药物的应用.

主要方法:

  • 使用Pandas和JupyterLab进行数据分析.
  • 采用随机森林和XG Boost回归算法.
  • 使用亚伯拉罕描述符预测的Kp:过度摩尔折射 (E),双极性/极化性 (S),键酸性/基本性 (A,B) 和麦克高温体积 (V).

主要成果:

  • 随机森林和XG Boost模型都显示了统计学上显著的关联.
  • 建立了溶解物描述符和皮肤透系数之间的预测关系.
  • 验证了ML技术对Kp估计的有效性.
关键词:
皮肤的透性描述者描述者是指描述者.熊猫是一只大熊猫.随机的森林随机的森林通过皮肤通过皮肤.

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结论:

  • 随机森林和XG Boost是有效的ML工具,用于预测皮肤透性.
  • 亚伯拉罕描述符是基于ML的Kp预测模型的宝贵输入.
  • 这种方法可以加速药物输送研究和开发.