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相关概念视频

Genome-wide Association Studies-GWAS01:11

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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.
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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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基于机器学习的预测用于使用多基因风险评分对中风患者进行分类.

Marco Piazzi, Yifei Wang, Andrew Hornback

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    此摘要是机器生成的。

    将遗传风险得分与临床数据相结合,可以显著改善中风预测模型. 这种方法增强了对高风险个体的早期识别,以实现个性化预防策略.

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    科学领域:

    • 遗传学和基因组学 遗传学和基因组学
    • 生物统计学和流行病学
    • 医疗保健中的机器学习

    背景情况:

    • 脑卒中是全球主要的健康问题,导致严重的残疾和死亡.
    • 虽然非遗传 (NG) 因素至关重要,但遗传倾向也会影响中风易感性.
    • 早期识别高风险个体是有效预防中风的关键.

    研究的目的:

    • 评估多基因风险评分 (PRS) 在机器学习 (ML) 模型中对中风风险预测的附加值.
    • 为了比较使用PRS加NG因子和单独使用NG因子的ML模型的预测准确度.
    • 为了确定影响中风风险预测转移的因素,与PRS集成.

    主要方法:

    • 开发了ML模型,利用英国生物库数据预测10年内发生的中风风险.
    • 综合多基因风险评分 (PRS) 与传统的非遗传 (NG) 临床变量.
    • 优化模型用于使用AUROC的预测准确性,分析变量影响.

    主要成果:

    • 结合PRS的ML模型表明,与单独NG因素相比,对中风风险的预测准确度提高.
    • 分析显示,在包括PRS后,预测中风概率发生了变化.
    • 确定了影响模型预测的关键人口统计,生物标志物和临床因素.

    结论:

    • 多基因风险评分为基于ML的中风预测提供了非遗传因素的显著附加值.
    • 将PRS与NG因素一起纳入常规实践,可以改善早期中风诊断和患者的治疗结果.
    • 对特定队列和可变相互作用的进一步研究可以完善个性化中风预防.