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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.5K
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...
13.5K

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

Toward Artificial Intelligence-driven Clinical Decision Support Tools in Rheumatology.

Rheumatic diseases clinics of North America·2026
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026
Same author

Predicting the timing of first sustained cognitive worsening in Alzheimer's disease using real-world clinical data and machine learning.

medRxiv : the preprint server for health sciences·2026
Same author

Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events.

Biometrics·2026
Same author

Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.

Communications medicine·2026
Same author

Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Classification Under Local Differential Privacy with Model Reversal and Model Averaging.

Journal of machine learning research : JMLR·2026
Same journal

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Journal of machine learning research : JMLR·2026
Same journal

Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.

Journal of machine learning research : JMLR·2026
Same journal

Unsupervised Tree Boosting for Learning Probability Distributions.

Journal of machine learning research : JMLR·2026
Same journal

A Two-Stage Penalized Least Squares Method for Constructing Large Systems of Structural Equations.

Journal of machine learning research : JMLR·2026
Same journal

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes.

Journal of machine learning research : JMLR·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

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

预先适应性半监督学习与应用到EHR表型化

Yichi Zhang1, Molei Liu2, Matey Neykov3

  • 1Department of Computer Science and Statistics, University of Rhode Island.

Journal of machine learning research : JMLR
|November 17, 2023
PubMed
概括
此摘要是机器生成的。

电子健康记录 (EHR) 数据可以促进疾病研究,但缺乏精确的表型信息. 一种新的半监督方法通过使用标记和弱标记数据来改进EHR表型,增强发现研究.

关键词:
高维稀疏回归的高维回归电子健康记录是电子健康记录.规范化 规范化 规范化半监督学习 半监督学习单一指数模型是一个单一的指数模型.

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
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

7.5K

相关实验视频

Last Updated: Jul 11, 2025

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
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
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

7.5K

科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算生物学 计算生物学
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 电子健康记录 (EHR) 为生物医学研究提供了丰富的数据,但由于表型信息不准确,这些数据未得到充分利用.
  • 对于EHR表型化,监督学习方法需要大量的标记数据集,这些数据集通常是不可用的.
  • 现有的方法在大型特征集和有限的黄金标准标记数据方面扎.

研究的目的:

  • 开发一种新的半监督 (SS) EHR表型化方法,以解决监督方法的局限性.
  • 从EHR数据中提高表型预测的准确性和通用性.
  • 利用小型标记和大型弱标记数据集进行增强的表型化.

主要方法:

  • 提出了一种半监督 (SS) 的EHR表型化方法,利用一个小的标记数据集和一个大的弱标记数据集.
  • 引入了先前适应性半监督 (PASS) 估计器,该估计器通过向衍生方向收缩来结合先前的知识.
  • 推导出非对称理论,以证明估计器的效率和稳定性,即使有不完美的先前信息.

主要成果:

  • 拟议的PASS估计器在模拟研究中表现出优于现有方法的优势.
  • 该方法在各种场景中被证明是有效和稳健的,包括那些质量不佳的预先信息.
  • 在一家主要的三级医院对三项现实世界EHR表型研究的验证证实了其实际实用性.

结论:

  • 开发的半监督 (SS) EHR表型化方法,特别是PASS估计器,显著提高了表型预测的准确性.
  • 这种方法有效地克服了EHR研究中小标记数据集的局限性.
  • 这些发现表明了利用电子健康记录数据推进发现研究的有希望的方向.