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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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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
316
Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Multiple Allele Traits01:49

Multiple Allele Traits

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The Concept of Multiple Allelism
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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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堆叠的神经网络用于预测多基因风险评分.

Sun Bin Kim1, Joon Ho Kang1, MyeongJae Cheon1

  • 1Genoplan Korea Inc., Seoul, Republic of Korea.

Scientific reports
|May 21, 2024
PubMed
概括

一个新的堆叠神经网络多原风险评分 (SNPRS) 通过整合多个模型来改善疾病易感性预测. 这种方法提高了对复杂遗传特征的传统多基因风险评分 (PRS) 的准确性.

科学领域:

  • 遗传学 遗传学 是一个
  • 计算生物学 计算生物学
  • 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) 对于使用全基因组关联研究 (GWAS) 预测疾病易感性非常有价值.
  • 由于相关的遗传变异,传统的PRS方法可能会遭受过度拟合和效果大小高估.
  • 现有的模型可能无法完全捕捉影响疾病风险的遗传因素的复杂相互作用.

研究的目的:

  • 引入一种新的堆叠神经网络多基因风险评分 (SNPRS),以克服传统PRS的局限性.
  • 通过整合多样化的遗传变异数据,提高遗传风险预测的准确性和细微差别.
  • 评估SNPRS的性能与大型遗传数据集中的现有方法相比.

主要方法:

  • 开发了SNPRS,SNPRS是一种合成来自多个神经网络的输出的方法,通过不同的p值值选择的变体进行训练.
  • 利用不同的p值值来捕捉更广泛的遗传变异谱.
  • 应用SNPRS到英国生物银行和韩国基因组和流行病学研究 (KoGES) 数据集进行验证.

主要成果:

  • 与传统的PRS模型相比,SNPRS显示出更高的预测准确性.
  • 这种新的方法在预测疾病易感性和定量特征方面优于孤立的深度神经网络.
关键词:
深度学习是一种深度学习.组合学习学习 组合学习多基因风险评分多基因风险评分.

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  • SNPRS有效地整合了来自更广泛的遗传变异信息,以改善风险评估.

    • 结论:

    • SNPRS代表了多基因风险预测方法的重大进步.
    • 堆叠神经网络方法提供了一种更强大,更准确的方法来评估遗传易感性.
    • 在基因研究和个性化医学中,SNPRS有望改进PRS的疗效和临床相关性.