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

Genetic Screens02:46

Genetic Screens

4.6K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.6K

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

Vitamin D deficiency in high-altitude populations: 25(OH)D level and association with systemic inflammation.

BMC nutrition·2026
Same author

Clinical Study of Ultrasound-Guided Modified Percutaneous Endoscopic Interlaminar Discectomy Combined With Medical Chitosan in Lumbar 4-5 Disc Herniation Treatment.

Orthopaedic surgery·2026
Same author

The landscape of cellular immune alteration in systemic lupus erythematosus.

Frontiers in immunology·2026
Same author

Physical activity profiles of attention-deficit/hyperactivity disorder symptoms among preschool children.

BMC pediatrics·2026
Same author

Dietary Index for Gut Microbiota and Osteoporosis Risk in Chinese Postmenopausal Women: A Case-Control Study.

International journal of women's health·2026
Same author

The impact of COVID-19 infection on the live birth rate in fresh embryo transfer cycles.

Frontiers in endocrinology·2026

相关实验视频

Updated: Apr 25, 2026

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

STLBRF:基于基因表达数据特征查标准化值的改进随机森林算法.

Huini Feng1, Ying Ju2, Xiaofeng Yin3

  • 1School of Mathematics and Statistics, Southwest University, Chongqing, China.

Briefings in functional genomics
|December 30, 2024
PubMed
概括

一个新的基于标准化值和循环的随机森林 (STLBRF) 算法可以从杂的生物统计数据中改进基因选择. 这种方法提高了对选定特征基因的准确性和控制,提供了可靠的生物标志物发现.

关键词:
生物标志物生物标志物特性 基因选择 基因选择改进了随机森林算法.噪音数据 噪音数据这是一个标准化值.

更多相关视频

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
00:09

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

21.6K
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

637

相关实验视频

Last Updated: Apr 25, 2026

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
Pooled CRISPR-Based Genetic Screens in Mammalian Cells
00:09

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

21.6K
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

637

科学领域:

  • 生物统计学 生物统计学
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 传统的随机森林 (RF) 算法在特征选择中与噪声和参数干扰作斗争.
  • 直接消除噪音可以在生物统计分析中引入显著的偏差.
  • 准确的特征基因选择对于生物标志物发现和表达分析至关重要.

研究的目的:

  • 开发一个改进的随机森林算法,用于强大的特征基因选择.
  • 解决传统射频在处理杂基因表达数据方面的局限性.
  • 提高生物统计应用中特征选择的准确性和控制.

主要方法:

  • 开发了一个新的基于标准化值和循环的随机森林 (STLBRF) 算法.
  • 集成的倒退消除和K折交叉验证,具有标准化的错误增量值.
  • 通过使用三个真实基因表达数据集,将STLBRF与,拉索,弹性网和传统RF进行比较.

主要成果:

  • 与现有方法相比,STLBRF在特征基因选择方面表现出更高的有效性.
  • 该算法可以更好地控制所选特征基因的数量.
  • 使用随机森林分类器的验证证实了STLBRF选择基因的可靠性.

结论:

  • 该STLBRF算法提供了一个强大的解决方案,用于特征基因选择在存在噪声.
  • 该方法为特征表达分析和生物标志物研究提供了可靠的技术支持.
  • STLBRF提高了基因选择的准确性和可控性,克服了传统的RF限制.