Jove
Visualize
联系我们

相关概念视频

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

Optimized Prostate Cancer Stage Classification Using XGBoost Based on Racial miRNA Expression.

Cancer diagnosis & prognosis·2026
Same author

Artificial Intelligence Design for Race-Based Prostate Cancer Stage Classification With Multilayer Perceptron: Feature Selection Optimization Approach.

JMIR formative research·2026
Same author

Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach.

JMIR bioinformatics and biotechnology·2025
Same author

Robust Logistic Regression-based Diagnosis Method of Prostate Cancer Using Optimized Feature Selection on Race Specific Gene-expression Datasets.

Cancer diagnosis & prognosis·2025
Same author

Correction: Design and development of an irrigation monitoring and control system based on blynk internet of things and thingspeak.

PloS one·2025
Same author

Design and development of an irrigation monitoring and control system based on blynk internet of things and thingspeak.

PloS one·2025
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

相关实验视频

Updated: Sep 18, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

675

通过基因表达数据使用机器学习进行种族特异性前列腺癌检测的框架:特征选择优化方法.

David Agustriawan1, Adithama Mulia1, Marlinda Vasty Overbeek1

  • 1Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Scientia Garden Jl. Boulevard Gading Serpong, Tangerang, ID.

JMIR bioinformatics and biotechnology
|June 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种特定于种族的机器学习模型,用于使用基因表达数据检测前列腺癌. 该模型在白人和非裔美国患者的前列腺癌诊断中取得了高准确性.

更多相关视频

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

16.7K
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 18, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

675
Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
13:19

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

16.7K
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.
  • 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:

  • 对于前列腺癌检测的机器学习模型显示高精度.
  • 以前的研究往往忽视了基因表达数据中的种族多样性和异常基因选择.

研究的目的:

  • 利用基因表达数据,开发一种用于前列腺癌诊断的特定种族分类方法.
  • 通过考虑人口多样性和强大的特征选择,解决先前研究的局限性.

主要方法:

  • 使用差异表达基因 (DEG) 分析,接收器操作特征 (ROC) 分析,以及用于特征选择的MSigDB验证.
  • 建筑支向量机 (SVM) 模型用于分类.

主要成果:

  • 一个具有139个基因特征的模型在白人患者中达到98%的准确性,在非洲裔美国患者中达到97%.
  • 一个只有9个基因特征的模型表现出强大的表现,白人的准确率为97%,非洲裔美国人的准确率为95%.

结论:

  • 确定了一种特定于种族的诊断方法来检测前列腺癌.
  • 强调了增强特征选择和机器学习的潜力,以在不同人群中提供公正的诊断工具.