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相关概念视频

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Factors Influencing Drug Absorption: Disease States and Pharmacology01:25

Factors Influencing Drug Absorption: Disease States and Pharmacology

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Multiple disease states can significantly influence the oral drug absorption process by affecting blood flow and the functionality of the gastrointestinal (GI) system. Various GI diseases, including conditions that alter GI motility, such as diarrhea, decreased acid secretions (achlorhydria), and infections, have been associated with reduced drug absorption.
Substances such as alcohol and specific drugs, including antineoplastics, can also negatively impact drug absorption. For instance,...
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Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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Drug Biotransformation: Overview01:16

Drug Biotransformation: Overview

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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Hazard Ratio01:12

Hazard Ratio

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
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相关实验视频

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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标签转移药物疾病协会在三个元路径中的标签转移.

Nam Anh Dao1, Manh Hung Le1, Xuan Tho Dang2

  • 1Electric Power University, Hanoi, Vietnam.

Evolutionary bioinformatics online
|September 16, 2024
PubMed
概括

这项研究引入了新的计算方法来预测药物-疾病相互作用,减少了昂贵的实验. 开发的机器学习模型有效地识别了生物网络中的潜在关联.

科学领域:

  • 计算生物学是一种计算生物学.
  • 药理学 药理学是指药理学的学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 识别药物疾病相互作用对于公共卫生和药物发现至关重要.
  • 确定这些相互作用的实验方法耗时且昂贵.
  • 许多潜在的药物疾病关联仍然未被发现,需要有效的计算方法.

研究的目的:

  • 开发新的计算方法来预测潜在的药物疾病关联.
  • 为了利用包含药物,蛋白质和疾病的异质生物网络.
  • 提高药物与疾病相互作用预测的准确性和效率.

主要方法:

  • 在异质生物网络 (药物-蛋白质-疾病) 中提出了三组新型元路径.
  • 为每个元路径设计了个别的机器学习模型.
  • 将这些模型整合到一个统一的学习框架中.

主要成果:

  • 在三个标准数据集上评估了方法:DrugBank,OMIM和Gottlieb的数据集.
  • 与EMP-SVD,LRSSL,MBiRW,MPG-DDA和SCMFDD等现有方法相比,表现出优越的性能.
  • 在关键性能指标中获得高分,包括曲线下的面积 (AUC),精度回忆曲线下的面积 (AUPR) 和F1分.
关键词:
药物疾病协会 药物疾病协会药物开发是药物的发展.药物重新定位 药物重新定位知识图嵌入式知识图嵌入式结构性的代表性.

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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相关实验视频

Last Updated: Jun 13, 2025

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结论:

  • 拟议的综合学习方法有效地预测了药物和疾病的关联.
  • 这种计算方法为实验方法提供了具有成本效益和效率的替代方案.
  • 这些发现有助于通过准确的相互作用预测推进药物发现和个性化医疗.