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相关概念视频

Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

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Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
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Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

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The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

296
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
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Factors Affecting Drug Response: Overview01:21

Factors Affecting Drug Response: Overview

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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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相关实验视频

Updated: May 23, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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DRExplainer:用定向图形卷积网络对药物反应预测的可量化的解释性.

Haoyuan Shi1, Tao Xu2, Xiaodi Li2

  • 1University of Science and Technology of China, Hefei, 230026, Anhui, China; School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, Anhui, China.

Artificial intelligence in medicine
|March 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了DRExplainer,这是一种用于预测癌症药物反应的新型深度学习模型. 它使用定向图形卷积网络来解释预测并识别个性化医学的关键生物特征.

关键词:
定向图形卷积网络 定向图形卷积网络药物反应预测药物反应预测.多个omics的多个omics.可以量化的解释性.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 机器学习在医学中的应用

背景情况:

  • 对治疗药物的癌症细胞系反应的准确预测对于推进个性化医学至关重要.
  • 现有的深度学习模型在整合多样化的生物数据和预测定向药物反应方面面临挑战.
  • 预测模型的可解释性对于临床决策至关重要.

研究的目的:

  • 提出DRExplainer,一个新的可解释的预测模型,用于预测癌症药物反应.
  • 通过整合多omics配置文件,药物化学结构和已知的反应,在一个有针对性的双边网络中提高预测准确性.
  • 为模型解释性提供一种可量化的方法,并确定驱动预测的关键生物特征.

主要方法:

  • 开发DRExplainer,一个定向图形卷积网络模型.
  • 建立一个有针对性的双边网络,整合细胞系多omics数据,药物化学结构和药物反应信息.
  • 实施一个面具学习机制,以识别相关的子图,以预测可解释性.
  • 创建一个基准真相基准数据集,以量化模型的可解释性.

主要成果:

  • 与最先进的预测方法和现有的基于图形的解释方法相比,DRExplainer表现出更高的性能.
  • 该模型成功识别了相关的子图,提高了药物反应预测的可解释性.
  • 案例研究验证了该模型在预测新药反应及其可解释性方面的有效性.

结论:

  • DRExplainer提供了一种有效和可解释的方法来预测癌症药物反应.
  • 该模型能够整合多样化的数据并提供可解释的见解,这对个性化医疗具有重大潜力.
  • 开发的可解释性方法提供了一种可量化的方法来理解基于生物特征的模型预测.