Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Gradient and Del Operator01:14

Gradient and Del Operator

2.5K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
2.5K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

58
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
58
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

41
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...
41
Graded Potential01:19

Graded Potential

3.6K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
3.6K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

101
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,...
101
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

89
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
89

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Network Clustering Approach Reveals Key Proteins and Biological Functions in the Response of Whiteleg Shrimp (Penaeus vannamei) to Acute Hepatopancreatic Necrosis Disease.

Marine biotechnology (New York, N.Y.)·2026
Same author

Graph latent diffusion-based molecular representation learning for enhanced generalization in molecular property prediction.

Journal of cheminformatics·2026
Same author

Self-supervised domain adaptation of protein language model based solely on positive enzyme-reaction pairs.

Computational and structural biotechnology journal·2025
Same author

Relationship between Antiviral Activity against Influenza A Virus Induced by Compound Combinations and Changes in the Physical Properties of Lipid Bilayers.

ACS pharmacology & translational science·2025
Same author

Implementation of a Conditional Latent Diffusion-Based Generative Model to Synthetically Create Unlabeled Histopathological Images.

Bioengineering (Basel, Switzerland)·2025
Same author

Field dynamics of the root endosphere microbiome assembly in paddy rice cultivated under no fertilizer input.

Plant & cell physiology·2025
Same journal

SpaceExpander: An Automated System for Drafting Markush Claims to Expand Chemical Space.

Molecular informatics·2026
Same journal

A Structure-Informed Atlas of Venom-Derived Peptides Reveals the Organization of Chemical Space.

Molecular informatics·2026
Same journal

ConGen: Targeted Molecule Generation Through Contrastive Learning and Latent Optimization.

Molecular informatics·2026
Same journal

Novel Molecules Generation Using Graph Generative Adversarial Networks.

Molecular informatics·2026
Same journal

An Attention-Driven Graph Transformer With Nonlinear Modeling and Neuro-Fuzzy Fusion for High-Order Toxic Molecular Graph Learning.

Molecular informatics·2026
Same journal

Molecular Modeling and Chemoinformatics in Ukraine.

Molecular informatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 6, 2025

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.1K

通过使用集成梯度来解释高斯过程模型.

Fan Zhang1, Naoaki Ono1,2, Shigehiko Kanaya1,2

  • 1Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.

Molecular informatics
|November 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的方法,通过使用集成梯度 (IG) 来解释高斯过程回归 (GPR) 预测. 该方法通过详细说明特征贡献来解释预测不确定性,增强模型的信任和理解.

关键词:
可以解释的人工智能AI这是高斯斯过程.集成梯度的综合梯度.

更多相关视频

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

7.8K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

相关实验视频

Last Updated: Jun 6, 2025

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
07:09

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions

Published on: May 2, 2019

6.1K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

7.8K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

科学领域:

  • 机器学习 机器学习
  • 人工智能的人工智能
  • 统计建模 统计建模

背景情况:

  • 高斯过程回归 (GPR) 提供了预测和置信区间,但缺乏可解释性,特别是对于其不确定性估计.
  • 现有的可解释AI (XAI) 方法难以解释GPR模型中预测的标准偏差.
  • 与GPR的深度学习集成显示了准确性的承诺,但加剧了可解释性挑战.

研究的目的:

  • 开发一种用于解释GPR预测的新方法,重点关注不确定性组成部分.
  • 通过量化特征对预测不确定性的贡献来提高GPR模型的可解释性.
  • 通过可解释的不确定性,提高对关键应用中的GPR模型的信任和理解.

主要方法:

  • 将集成梯度 (IG) 方法与高斯过程回归 (GPR) 结合起来.
  • 通过评估每个解释变量的对预测的贡献来评估特征重要性.
  • 对后方分布的标准偏差进行分析,以分解预测不确定性.

主要成果:

  • 提出的基于IG的方法通过将不确定性归因于个别特征贡献,成功地解释了GPR预测.
  • 这种方法提供了预测不确定性的详细分解,突出了有影响力的变量.
  • 该方法量化了特征特定的不确定性,为模型可靠性提供了洞察力.

结论:

  • 新的IG-GPR解释方法增强了对GPR模型行为和预测不确定性的理解.
  • 这种技术通过使模型的决策过程更加透明,增加了对GPR预测的信任.
  • 这种方法在需要高可解释性以及预测准确性的领域特别有价值.