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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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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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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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Pleiotropy01:33

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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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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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DBNX:一种机器学习方法,用于组合多基因风险得分和非遗传因素.

Xiangzhe Yuan, Chonghao Wang, Shuqin Zhu

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    概括
    此摘要是机器生成的。

    这项研究引入了一种新的深度信念网络 (DBN) 模型,用于汇总多基因风险得分 (PRS) 并将其与生活方式因素相结合,提高疾病预测的准确性.

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    科学领域:

    • 遗传学 遗传学 是一个
    • 生物信息学是一种生物信息学.
    • 机器学习 机器学习

    背景情况:

    • 多基因风险评分 (PRS) 评估了使用多种变异来预测疾病的遗传易感性.
    • 目前的PRS方法在疾病和人口特异性方面存在局限性,并且经常忽视非遗传因素.
    • 组合方法结合多个PRS可以提高预测,但往往需要训练数据.

    研究的目的:

    • 开发一种无监督的方法来聚合多种多基因风险评分 (PRS).
    • 创建一个模型,将PRS与非遗传因素集成为全面的综合风险评分 (CRS).
    • 通过利用遗传和非遗传风险因素来提高疾病预测的准确性.

    主要方法:

    • 使用无监督的深信网络 (DBN) 来从各种方法中汇总PRS.
    • DBN方法不需要培训数据,可以直接组合现有的PRS.
    • DBNX模型将DBN与XGBoost结合起来,以整合PRS和非遗传因素,生成一个CRS.

    主要成果:

    • DBN的性能与监督组合方法相提并论,在小型数据集上表现优于超级学习者.
    • 在使用英国生物银行数据集预测四种疾病时,DBNX表现优于其他组合方法.
    • DBNX模型有效地整合了遗传和非遗传因素,以获得更准确的综合风险评分.

    结论:

    • 无监督的DBN为PRS聚合提供了有效的替代方案,在某些场景中表现优于监督方法.
    • 通过结合多基因风险和非遗传因素,DBNX提供了一个强大的框架来生成综合风险评分.
    • 通过结合多因素数据,DBNX模型代表了个性化疾病风险预测的重大进步.