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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

154
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
154
Overview of Biostatistics in Health Sciences01:19

Overview of Biostatistics in Health Sciences

469
Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
469

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

Clinical validation of tissue and liquid companion diagnostics for BRAF V600E detection in non-small cell lung cancers from the PHAROS study.

Cancer research communications·2026
Same author

A multiplexed targeted mass spectrometric assay for quantifying obesity- associated biomarkers in human plasma.

Research square·2026
Same author

Three-dimensional printing of multilayer stretchable electronics with inclined interconnect accesses.

Nature communications·2026
Same author

Development and validation of a simplified machine learning model based on T-SPOT.TB and routine clinical data for the diagnosis of tuberculous pleural effusion.

Journal of thoracic disease·2026
Same author

<i>Hex</i>-MASP for Mapping the Whole-tissue Spatial Proteome and the Intra-brain Distribution of Monoclonal Antibodies.

bioRxiv : the preprint server for biology·2026
Same author

Effects of a low-load multi-component training program with blood flow restriction versus the same program without blood flow restriction on muscle thickness and functional outcomes in physically inactive young adults: randomized controlled trial.

Frontiers in physiology·2026

相关实验视频

Updated: Jul 12, 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

7.5K

统计机器学习在生物标志物选择中的应用.

Ritwik Vashistha1, Zubdahe Noor2, Shibasish Dasgupta3,4

  • 1Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, TX, USA.

Scientific reports
|October 26, 2023
PubMed
概括

这项研究评估了来自JAVELIN Bladder 100试验的晚期泌尿器癌 (aUC) 患者生物标志物识别的可变选择方法. 一些方法显示出希望,但数据中的高对线性为生物标志物发现带来了挑战.

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

相关实验视频

Last Updated: Jul 12, 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

7.5K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

科学领域:

  • 在瘤学瘤学.
  • 生物统计学 生物统计学
  • 基因组医学是基因组医学.

背景情况:

  • 晚期泌尿腺癌 (aUC) 治疗从维持疗法中获益,正如JAVELIN Bladder 100试验所示.
  • 鉴定生物标志物对于aUC的精密医学至关重要,但由于基因组数据中的高对线性和低信号存在挑战.
  • 由于JAVELIN Bladder 100数据集的特性,使用标准变量选择方法很难可靠地识别生物标志物.

研究的目的:

  • 评估各种可变选择方法在发现aUC患者的预后和预测生物标志物的性能.
  • 为了比较处罚回归,随机生存森林和贝叶斯变量选择方法.
  • 为贝叶斯方法提出一个修改的贝叶斯信息标准 (BIC) 值规则.

主要方法:

  • 一项模拟研究评估了高维数据的流行的变量选择技术.
  • 使用了处罚回归模型,随机生存森林和贝叶斯变量选择.
  • 这些方法应用于JAVELIN Bladder 100数据集,以确定与生存相关的生物标志物.

主要成果:

  • 变量选择方法通常具有较低的错误发现率,但在高对线性方面遇到了困难.
  • 与拉索相关的方法在JAVELIN Bladder 100数据中确定了潜在的生物相关变量.
  • 随机搜索变量选择和随机生存森林显示了数据集对线性和低信号的限制.

结论:

  • 对于在高维,直线aUC数据中发现生物标志物而言,没有一种单一变量选择方法是普遍优越的.
  • 需要进一步的研究来开发新的可变选择方法,以便在这种患者群体中进行可靠的生物标志物识别.
  • 这项研究强调了在晚期泌尿腺癌中发现生物标志物的复杂性,并为未来的研究方向提供了信息.