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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43

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

Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets.

bioRxiv : the preprint server for biology·2026
Same author

Nucleus-level thalamic organization anchors multimodal signatures of thalamocortical maturation.

bioRxiv : the preprint server for biology·2026
Same author

Widespread synaptic density loss in schizophrenia follows molecular and network architecture.

Molecular psychiatry·2026
Same author

Molecular and cellular correlates of human cortical lateralization.

Communications biology·2026
Same author

Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy.

Epilepsia·2026
Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jul 8, 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

763

多层元匹配:将表型预测模型从多个数据集转换为小数据.

Pansheng Chen1,2,3, Lijun An1,2,3, Naren Wulan1,2,3

  • 1Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore.

bioRxiv : the preprint server for biology
|December 18, 2023
PubMed
概括

新的元匹配方法改善了从大脑连接数据中预测特征的方法,特别是对于小型数据集. 多层元匹配提供了最佳的性能,超过了神经科学研究的现有方法.

更多相关视频

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.5K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

相关实验视频

Last Updated: Jul 8, 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

763
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.5K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

科学领域:

  • 神经科学是一个神经科学.
  • 计算生物学 计算生物学
  • 数据科学数据科学数据科学

背景情况:

  • 休息状态功能连接 (RSFC) 对于预测个体表型特征至关重要.
  • 大数据集可以提高预测的准确性,但小型数据集通常是临床或特定研究所必需的.
  • 之前的元匹配方法在将模型从大数据集转换为小数据集方面表现出了希望.

研究的目的:

  • 引入和评估两个新的元匹配变体:"元匹配与数据集堆叠"和"多层元匹配".
  • 增强预测模型从多个多样化的源数据集转换为用于表型预测的小目标数据集.

主要方法:

  • 开发并测试了"元匹配与数据集堆叠"和"多层元匹配"技术.
  • 在五个源数据集 (86236,834名参与者) 上训练翻译模型,以预测人类连接组项目年轻成年人 (HCP-YA) 和HCP-Aging数据集中的表型.
  • 与原始元匹配,内核回归 (KRR) 和经典转移学习相比,性能比较.

主要成果:

  • 多层元匹配与数据集堆叠的元匹配相比,多层元匹配显示出适度的优势.
  • 这两种新变体的表现明显优于原始的元匹配方法和经典的转移学习.
  • 核回归 (KRR) 在非常小的样本方案 (<50名参与者) 中被发现比经典转移学习更有效.

结论:

  • 拟议的多层元匹配方法有效地将预测模型转化为不同大小的数据集,特别有利于小的目标数据集.
  • 这些先进的元匹配技术比使用RSFC进行表型预测的传统方法提供了实质性的改进.
  • 多层元匹配模型是公开的,促进了该领域的进一步研究.