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相关概念视频

Classification of Illness01:17

Classification of Illness

7.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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Multiple Bar Graph01:07

Multiple Bar Graph

5.3K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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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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Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
509
Genomics02:02

Genomics

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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...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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...
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相关实验视频

Updated: Jul 27, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

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在疾病关系提取图表上的多式学习.

Yucong Lin1, Keming Lu2, Sheng Yu3

  • 1Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China; Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.

Journal of biomedical informatics
|June 5, 2023
PubMed
概括
此摘要是机器生成的。

作为一种多式联络方法,REMAP通过将不完整的知识图与医疗文本相融合来增强疾病关系提取. 这种方法提高了准确性和F1分数,使得更好地发现疾病联系.

关键词:
疾病关系提取 提取图形神经网络是一个神经网络.知识图是知识图.语言模型 语言模型医疗关系提取 提取多模式学习是多模式学习.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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相关实验视频

Last Updated: Jul 27, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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科学领域:

  • 人工智能的人工智能
  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.

背景情况:

  • 疾病知识图表对于组织复杂的疾病信息至关重要.
  • 由于文本中的分散数据和不完整的图形,提取准确的疾病关系是具有挑战性的.
  • 为了构建全面的知识图表,需要多模式数据融合.

研究的目的:

  • 引入REMAP,一种用于疾病关系提取的新型多式联络方法.
  • 通过整合各种数据源来提高疾病知识图的准确性和完整性.
  • 为了实现强大的关系提取,即使缺少数据模式.

主要方法:

  • 雷马普将不完整的知识图和医学文本数据集联合嵌入到潜伏向量空间中.
  • 一个脱的模型结构允许单模推理,解决缺失的数据场景.
  • 该方法应用于一个大规模的疾病知识图表和文本库.

主要成果:

  • REMAP提高了基于语言的疾病关系提取,精度为10.0%,F1得分为17.2%.
  • 该方法在准确度方面超过了以图表为基础的方法8.4%,在推新的关系方面超过了F1得分的10.4%.
  • 知识图和语言数据的融合显著增强了关系提取.

结论:

  • 雷马普为疾病关系提取提供了一种灵活有效的多式联运战略.
  • 该方法成功地将结构化知识与非结构化文本数据集成在一起.
  • 这种模型有助于发现,访问和评估疾病概念关系.