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

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
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.4K
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...
14.4K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

152
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
152
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

596
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...
596
Multiple Regression01:25

Multiple Regression

3.2K
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...
3.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

726
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
726

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

Disrupted erythrocyte S1P-eNOS axis promotes hypoxia, hypertension and fibrosis in obstructive sleep apnoea-hypopnoea syndrome.

European heart journal·2026
Same author

Differences in disease burden between neuromyelitis optica spectrum disorder and multiple sclerosis in South China.

Multiple sclerosis journal - experimental, translational and clinical·2026
Same author

Interpretable machine learning for cattle breed classification and SNP prioritization.

Genetics, selection, evolution : GSE·2026
Same author

Proximal regularization of deep residual neural networks applied to high-dimensional genomic data.

Briefings in bioinformatics·2026
Same author

Deep medullary vein drainage and unfavorable outcomes in stroke patients after endovascular therapy: a retrospective study.

BMC neurology·2026
Same author

Asian expert consensus on high-quality hypertension management.

Hypertension research : official journal of the Japanese Society of Hypertension·2026
Same journal

Research on multi-trait genome association study method based on Shannon information entropy.

BMC bioinformatics·2026
Same journal

A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.

BMC bioinformatics·2026
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Sep 16, 2025

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

914

使用封闭的剩余变量选择神经网络进行多任务基因组预测.

Yuhua Fan1, Patrik Waldmann2

  • 1Research Unit of Mathematical Sciences, University of Oulu, P.O. Box 8000, Oulu, 90014, Finland.

BMC bioinformatics
|July 7, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了封闭的残留变量选择神经网络 (GRVSNN),通过结合基因组和血统数据来改善全基因组预测 (GWP). GRVSNN提高了预测准确性和可解释性,优于现有模型.

关键词:
深度学习是一种深度学习.有门的残留神经网络.基因组选择 基因组选择变量选择 变量选择

更多相关视频

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

522
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

7.6K

相关实验视频

Last Updated: Sep 16, 2025

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

914
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

522
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

7.6K

科学领域:

  • 基因组学就是基因组学.
  • 机器学习 机器学习
  • 量化遗传学 量化遗传学

背景情况:

  • 高通量测序可以实现全基因组预测 (GWP),但整合血统数据传统上需要计算密集的方法.
  • 现有的方法很难将血统基因组集成扩展到灵活的机器学习方法.

研究的目的:

  • 通过在多任务学习中实施封闭的残余变量选择神经网络 (GRVSNN) 来提高基因组预测的准确性和可解释性.
  • 将低级血统信息与使用GRVSNN的基因组标记器集成在一起,将其性能与传统回归和深度学习 (DL) 模型进行比较.

主要方法:

  • 开发并应用了一个封闭的残余变量选择神经网络 (GRVSNN) 模型,用于多任务基因组预测.
  • 基于血统的综合关系矩阵 (低级信息) 与基因组标记.
  • 评估了GRVSNN在真实世界的数据集上从松,老鼠和猪.

主要成果:

  • 在预测准确性方面,GRVSNN显著优于贝叶斯回归和拉索网等传统模型.
  • 在预测和真实表型之间实现了较低的平均二次误差 (MSE) 和更高的皮尔森 (r) 和距离相关性 (dCor).
  • 通过选择更少的遗传标记和血统加载,GRVSNN证明了更好的解释性.

结论:

  • GRVSNN框架提供了一种计算有效的方法,用于整合血统和基因组数据,以改善基因组预测.
  • 它的多任务预测能力有可能促进农业的选择和精准医学中的疾病预测.