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相关概念视频

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.5K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

182
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
182
Factors Affecting Activity Coefficient01:17

Factors Affecting Activity Coefficient

832
The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...
832
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.5K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.4K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.4K
Contingency Table01:29

Contingency Table

2.5K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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相关实验视频

Updated: Jul 20, 2025

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
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Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

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使用反事实和Shapley值解释多类复合活动预测.

Alec Lamens1, Jürgen Bajorath1

  • 1Department of Life Science Informatics, B-IT, LIMES Program, Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany.

Molecules (Basel, Switzerland)
|July 29, 2023
PubMed
概括

本研究介绍了一种测试系统,用于理解机器学习 (ML) 在药物发现中的预测. 它使用可解释的人工智能 (XAI) 方法,为化合物活动预测提供化学直观的理由.

关键词:
在SHAP中,价值是SHAP值.相反的事实 (counterfactuals) 是一种反事实.双重目标化合物是双重目标化合物.可解释的人工智能机器学习是机器学习.多类活动预测模型单一目标化合物是一种单一目标化合物.

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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相关实验视频

Last Updated: Jul 20, 2025

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习是机器学习.
  • 人工智能的人工智能是人工智能.

背景情况:

  • 制药研究中的机器学习 (ML) 模型通常充当"黑子",阻碍其用于指导实验工作的采用.
  • 可解释的ML (XML) 是可解释的人工智能 (XAI) 的一个子集,正在获得吸引力,以提高ML预测的可解释性.

研究的目的:

  • 开发和验证用于合理化多类化合物活性预测模型的测试系统.
  • 在制药研究中增强对ML模型的理解和信任.

主要方法:

  • 整合两个XAI方法:反事实 (CF) 和沙普利增量解释 (SHAP) 进行特征相关性分析.
  • 开发一个测试系统来分析多类化合物活性预测模型.
  • 识别改变预测结果的小型化合物修改.

主要成果:

  • 开发的系统成功合理化了多类化合物活动预测模型.
  • 反事实和SHAP分析确定了特定的复合修改,扭转了预测的活动.
  • 与CF和SHAP结合的特征映射为模型预测提供了化学直观的解释.

结论:

  • 结合使用CF和SHAP提供了一种强大的方法来解释化合物活性评估中的ML预测.
  • 这种方法促进了对模型决策的更深入的化学理解,促进了ML在药物发现中的使用.
  • 该系统为合理化化合物修改和指导实验设计提供了可操作的见解.