Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

9.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.1K
Multiple Regression01:25

Multiple Regression

2.9K
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...
2.9K
Regression Analysis01:11

Regression Analysis

5.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.6K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

168
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
168
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

12.5K
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...
12.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Statistics and data science unlock the predictive power of quantitative genetics.

Frontiers in plant science·2026
Same author

Multimodal genomic prediction is not a buzzword: why modern plant breeding must integrate genomics, enviromics, and phenomics.

G3 (Bethesda, Md.)·2026
Same author

Comparing statistical 'phenomic prediction' models for remote-sensing-based phenotyping of maize susceptibility to common rust.

Plant phenomics (Washington, D.C.)·2026
Same author

Pharmacological treatment patterns, factors associated with glycemic control, and renal function parameters in a real-world cohort of Hispanic adults with type 2 diabetes.

Biomedical reports·2026
Same author

Multimodal deep learning improves cross-environment prediction of durum wheat yield components.

BMC plant biology·2026
Same author

Nonlinear genomic selection index accelerates multi-trait crop improvement.

Nature communications·2026

相关实验视频

Updated: Jun 7, 2025

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

7.4K

完善处罚回归:一种用于优化基因组预测中的规范化参数的新方法.

Abelardo Montesinos-López1, Osval A Montesinos-López2, Federico Lecumberry3

  • 1Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara 44430, Jalisco, México.

G3 (Bethesda, Md.)
|November 9, 2024
PubMed
概括

一种调整Ridge回归的新方法提高了基因组预测的准确性. 这种方法通过优化处罚参数来增强植物育种中的育种价值估计,从而在预测性能方面取得了显著的收益.

关键词:
基因 捕食者 捕食者共享数据资源共享数据资源育种价值 育种价值 育种价值基因组预测 基因组预测受到惩罚的回归回归.植物育种 植物育种脊回归的回归方法

更多相关视频

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

643
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

10.1K

相关实验视频

Last Updated: Jun 7, 2025

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

7.4K
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

643
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

10.1K

科学领域:

  • 遗传学 是一个遗传学.
  • 量化遗传学 量化遗传学
  • 生物信息学是一种生物信息学.

背景情况:

  • 基因组选择 (GS) 对于有效估计育种价值至关重要.
  • 回归是一种流行的基因组预测方法,但它的性能取决于最佳的惩罚参数调整.

研究的目的:

  • 引入一种新的,更有效的方法来选择基因组预测的Ridge回归中的最佳惩罚参数.
  • 通过使用现实世界数据集来评估拟议方法与传统方法的性能.

主要方法:

  • 在Ridge回归中开发一种用于最佳惩罚参数选择的新型算法.
  • 在14个真实植物育种数据集中对拟议方法和常规方法进行比较分析.

主要成果:

  • 拟议的方法在14个数据集中的13个中超过了传统方法.
  • 在数据集中观察到56.15%的预测准确度 (皮尔森相关性) 的显著增长.
  • 在正常化平均平方误差方面没有发现显著的增长.

结论:

  • 这种新的惩罚参数选择方法显示了在基因组预测中改善Ridge回归的巨大潜力.
  • 采用这种方法可以提高植物育种计划中候选品种的选择.
  • 这些发现支持高级参数调整的实用性,以实现更准确的基因组评估.