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相关概念视频

Polygenic Traits01:18

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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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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Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Human Genetics01:28

Human Genetics

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
516
Heritability01:06

Heritability

188
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
188
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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相关实验视频

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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探索深度学习方法用于多基因风险评分估计的应用.

Steven Squires1, Michael N Weedon1, Richard A Oram1,2

  • 1Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, United Kingdom.

Biomedical physics & engineering express
|February 28, 2025
PubMed
概括

深度学习 (DL) 模型可以准确地生成多基因风险得分 (PRS),即使数据有限或遗传信息缺失. 这些模型在改善用于临床和研究应用的PRS生成方面表现有前途.

关键词:
深度学习是一种深度学习.遗传学 遗传学 遗传学 是一个机器学习是机器学习.多基因风险得分的多基因风险得分.精准医学是一门精准医学.

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科学领域:

  • 遗传学 遗传学 是一个
  • 人工智能的人工智能
  • 生物信息学是一种生物信息学.

背景情况:

  • 多基因风险评分 (PRS) 对于总结遗传信息至关重要.
  • 在此之前,深度学习 (DL) 对PRS生成的影响有限.
  • 本研究探讨了DL在增强PRS创建方面的潜力.

研究的目的:

  • 调查深度学习 (DL) 如何改善多基因风险评分 (PRS) 的生成.
  • 评估DL模型在重建人类编程的PRS和从单个模型生成多个PRS中的性能.
  • 评估DL处理缺少遗传数据和性能限制的能力.

主要方法:

  • 在现有PRS上使用英国生物库数据培训DL模型.
  • 评估用于PRS复制和多PRS生成的DL模型.
  • 评估DL模型性能,减少训练数据和缺失单核酸多态 (SNP).

主要成果:

  • DL模型实现了多个PRS的近乎完美的生成,性能损失最小,即使训练数据减少.
  • 对于缺失的SNP,DL模型改善了与传统PRS (AUC0.798) 相比的病例和人群分离 (AUC0.847).
  • DL模型证明了可转移性和寿命.

结论:

  • 深度学习 (DL) 可以准确生成多基因风险得分 (PRS),包括从单个模型中获得多个得分.
  • 在改善PRS生成方面,DL模型显示出有前途,特别是在处理缺失的遗传数据时.
  • 进一步的进步可能需要额外的输入数据.