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相关概念视频

Infection01:20

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When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
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Urinary Tract Infection II: Pathophysiology01:25

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Stages of infection describe what happens to a susceptible host once a pathogen invades the human body. The stages of infection are incubation, prodromal, illness, stage of decline, and convalescence. The incubation stage is the period from exposure to a pathogen until symptoms start. The infected person is unaware of impending illness as the pathogens grow and multiply within the body. The duration may vary depending on the type of infection. The incubation period of measles averages ten to...
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基础科学和病原发生学

Eun Hye Lee1,2, Taeho Jo1,3

  • 1Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.

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概括
此摘要是机器生成的。

一个新的深度学习模型使用全基因组测序数据准确预测阿尔茨海默病痴呆症. 这种基于变压器的方法整合了本地和遥远的遗传关系,以改善早期检测.

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 神经科学是一个神经科学.

背景情况:

  • 早期预测阿尔茨海默病 (AD) 对于及时干预至关重要.
  • 基因组数据为AD预测提供了一个有前途的途径,但其高维度存在挑战.
  • 深度学习 (DL) 模型非常适合分析复杂的基因组数据集.

研究的目的:

  • 开发一种使用变压器架构的新型深度学习模型.
  • 仅从全基因组测序 (WGS) 数据预测阿尔茨海默病 (AD) 痴呆症.
  • 调查模型整合本地和遥远遗传关系的能力,以提高预测准确度.

主要方法:

  • 分析了来自ADNI和ADSP-FUS1-ADNI-WGS-2的1,050个全基因组测序 (WGS) 数据集.
  • 开发一个三步DL模型:基于窗口的注意力,注释增强分析和图形卷积网络 (GCN) 集成.
  • 专注于WGS数据中的1,000,000个基对部分,这些数据围绕着染色体19上的APOE基因.

主要成果:

  • 与基于窗口的步骤 (AUC = 0.53) 相比,完整的DL模型实现了显著的预测准确性 (AUC = 0.68).
  • 该模型确定了与AD痴呆症相关的关键单核酸多态 (SNP),包括新的发现.
  • 突出了位于APOE/APOC基因内或附近的顶级SNPsrs429358,rs120074114,rs120074111和rs5122.

结论:

  • 开发的DL模型有效地对WGS数据进行多尺度分析,以改善AD痴呆症预测.
  • 变压器架构促进了局部和远程遗传关系的整合,增强了预测能力.
  • 这项研究为利用DL模型与WGS数据在早期AD预测和高效数据处理中奠定了基础.