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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

69
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...
69
Genomics02:02

Genomics

36.3K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
36.3K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

99
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
99
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

128
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...
128
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

62
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
62
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

143
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,...
143

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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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路径集成:基于路径的多主题数据集成的多变量建模方法.

Cecilia Wieder1, Juliette Cooke2, Clement Frainay2

  • 1Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London, United Kingdom.

bioRxiv : the preprint server for biology
|January 23, 2024
PubMed
概括
此摘要是机器生成的。

通过分析生物途径,PathIntegrate提供了一种用于多omics数据集成的新方法. 这种方法增强了信号检测,并为复杂的生物系统提供了可解释的见解.

科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

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背景情况:

  • 多学科数据集成对于理解复杂的生物系统至关重要.
  • 当前的方法经常产生列表或集群,需要广泛的解释.
  • 解释高维的多维数据仍然是一个重大挑战.

结论:

  • PathIntegrate提供了一个可解释的框架,用于多omics数据集成.
  • 方便复杂,高维数据集的分析.
  • 可方便地解释生物过程从多omics研究.