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相关概念视频

Polygenic Traits01:18

Polygenic Traits

66.0K
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...
66.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
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...
81
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
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...
64
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

386
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...
386
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96

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相关实验视频

Updated: Jul 23, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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生物库规模的方法和预测,用于机器学习的稀疏多基因预测.

Timothy G Raben1, Louis Lello2,3, Erik Widen2,3

  • 1Department of Physics and Astronomy, Michigan State University, Michigan, USA. rabentim@msu.edu.

Scientific reports
|July 19, 2023
PubMed
概括
此摘要是机器生成的。

训练数据的大小是多基因分数的关键. 稀有机器学习,特别是LASSO,表现良好,性能主要来自数据量,而不仅仅是算法. 未来的预测者显示出跨祖先的承诺.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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相关实验视频

Last Updated: Jul 23, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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科学领域:

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

背景情况:

  • 多基因分数对于预测复杂特征至关重要.
  • 稀缺的机器学习算法被广泛用于模型训练.
  • 了解跨不同遗传祖先的模型性能是必不可少的.

研究的目的:

  • 用稀疏的机器学习算法训练的线性模型的性能.
  • 评估训练集大小,遗传祖先和训练方法对多基因分数表现的影响.
  • 开发一种基于数据大小的预测性绩效预测的新方法.

主要方法:

  • 使用稀疏的机器学习算法 (例如,LASSO) 构建多基因分数.
  • 检查性能指标作为训练数据大小和遗传祖先的函数.
  • 调查交叉祖先预测器的性能.
  • 开发基于数据大小的AUC和相关性预测方法.

主要成果:

  • 预测器性能最依赖于训练数据大小,从算法改进中获得的边际收益很小.
  • 拉索的性能与其他领先的方法相美.
  • 在一个祖先群体上训练的预测者在其他群体上应用时显示不同的表现.
  • 一种新的投影方法准确地预测了性能限制,并与理论预测保持一致.

结论:

  • 训练数据的数量是多基因分数表现的主要驱动因素.
  • 拉索是建立多基因分数的强大而有效的方法.
  • 未来的多基因分数,如台湾精准医学倡议的分数,预计将在不同人群中实现高预测准确度.