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

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.4K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.4K

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

Expression of a recombinant DIVA antigen for differential diagnosis of H7N9 subtype avian influenza virus infected and vaccinated chickens.

Protein expression and purification·2026
Same author

Effects of mind-body training on upper-limb function in stroke patients: a multilevel dose-response meta-analysis.

Frontiers in medicine·2026
Same author

Regulation of EIF5A and hypusination by p53 determines colorectal cancer cell fitness.

Cancer letters·2026
Same author

One case of facial salivary gland secretory carcinoma and a literature review.

International journal of surgery case reports·2026
Same author

Terminal-Edge-Cloud Collaborative Computation Offloading and Resource Allocation Strategy Based on Improved Mayfly Algorithm for District Heating Systems.

Sensors (Basel, Switzerland)·2026
Same author

Fe-Enhanced Proton Capture on Boron Nitride Surfaces for Improved Photocatalytic Methane Conversion to C1 Chemicals.

Small science·2026
Same journal

The lncRNA-m6A axis in cancer: a bidirectional regulatory network in tumor progression and therapeutic resistance.

Journal of translational medicine·2026
Same journal

Repurposing cepharanthine as a radiosensitizer in esophageal squamous cell carcinoma through dual metabolic intervention and direct targeting of p70s6K.

Journal of translational medicine·2026
Same journal

Cellular crosstalk and signaling networks in the rheumatoid arthritis synovial microenvironment.

Journal of translational medicine·2026
Same journal

Pilot spatial transcriptomics of dental pulpitis suggests immune-fibroblast profiling linked to reversibility.

Journal of translational medicine·2026
Same journal

Beyond semen analysis: in men with normal semen parameters telomere attrition and oxidative imbalance distinguish those fertile from those with infertility.

Journal of translational medicine·2026
Same journal

Dual-block HER2 assessment reveals clinically relevant intratumoral heterogeneity in gynecologic cancers: a single-center landscape analysis.

Journal of translational medicine·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.0K

一个多代表性的深度学习框架,用于准确的多癌症分类.

Guojing He1,2, Xiao Yang2, Wang Yu2

  • 1College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, No. 2 Chongwen Road, Chongqing, 400065, China.

Journal of translational medicine
|November 19, 2025
PubMed
概括
此摘要是机器生成的。

GraphVar是一个新的深度学习框架,通过整合基因组和成像数据,准确地分类多种癌症类型. 这种可解释的工具有助于精确诊断和治疗策略.

关键词:
深度学习是一种深度学习.多重代表性的多重代表性变压器变压器变压器变种 变种 变种 变种

更多相关视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

相关实验视频

Last Updated: Jan 11, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
10:37

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.2K

科学领域:

  • 在瘤学瘤学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 准确的多种癌症分类对于瘤学的诊断,治疗和预后至关重要.
  • 现有的方法往往侧重于有限的癌症类型和单个基因组数据模式.
  • 需要先进的框架,整合多样化的基因组特征,以进行全面的癌症分类.

研究的目的:

  • 开发和评估GraphVar,一个用于多种癌症分类的新型深度学习框架.
  • 整合互补的突变衍生成像和数值基因组特征.
  • 通过利用多代表性学习来推进癌症分类.

主要方法:

  • GraphVar使用了多表示深度学习方法,集成空间变异地图 (图像) 和数值基因组特征.
  • 一个ResNet-18骨干提取图像特征,一个变压器编码器处理数值配置文件,一个融合模块结合模式.
  • 使用Grad-CAM评估可解释性,并通过KEGG通路丰富分析验证功能相关性.

主要成果:

  • 在33种癌症类型的10,112名患者队列中,GraphVar实现了高性能,精度,回忆,F1得分和准确性都超过了99.8%.
  • 格拉德-CAM分析表明,该模型能够精确地确定生物相关的基因组模式.
  • 凯格路径分析证实了GraphVar识别基因在特定癌症类型 (KIRC,BRCA) 中的生物学意义.

结论:

  • GraphVar是一个强大的和可解释的框架,用于准确的多种癌症分类.
  • 该模型的高性能和识别功能相关基因组签名的能力支持其用于精确诊断的潜力.
  • 需要进一步的翻译研究来探索GraphVar在指导治疗策略中的临床实用性.