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

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.1K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.1K

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

A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.

Journal of clinical medicine·2026
Same author

An Explainable Multi-Scale Deep Learning Framework for Multi-Class Brain MRI Classification.

Diagnostics (Basel, Switzerland)·2026
Same author

A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.

Diagnostics (Basel, Switzerland)·2026
Same author

Integrated deep learning model for multi-label retinal disease diagnosis.

Scientific reports·2026
Same author

Advancing Arabic automated essay scoring through cross-encoder BERT models and interpretable explanations.

Scientific reports·2026
Same author

A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.

PloS one·2026

相关实验视频

Updated: Jan 18, 2026

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.2K

整合使用基于关联的图表注意力网络的多组学数据,用于低级质瘤的亚型分类.

Eman Mohammed Hamid1, Murtada K Elbashir2, Nosiba Yousif Ahmed1

  • 1Department of Computer Science, Faculty of Mathematical and Computer Science, University of Gezira, Wad Madani, Sudan.

Discover oncology
|January 16, 2026
PubMed
概括

深度学习模型BioGAT-LGG使用多omics数据准确地分类低级质瘤 (LGG) 亚型. 它确定了针对个性化癌症治疗和决策的新型生物标志物.

关键词:
生物标记物识别方法癌症亚型分类 癌症亚型分类基于相关性的图表.在GATv2中,GATv2是什么?基因本体学是基因的本体学.在KEGG的路径中.在LGGLGGLGGLGGLGGLGGLGGLGGLGGL多个omics数据数据的数据.

更多相关视频

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

1.2K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

相关实验视频

Last Updated: Jan 18, 2026

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.2K
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

1.2K
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

科学领域:

  • 在瘤学瘤学.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 准确的癌症亚型分类对于开发个性化疗法至关重要.
  • 现有的方法往往依赖于外部的生物数据,限制了它们的范围.
  • 整合多学科数据提供了一种全面的方法来理解癌症的复杂性.

研究的目的:

  • 开发一个深度学习框架,BioGAT-LGG,用于低级质瘤 (LGG) 亚型分类.
  • 通过整合多omics数据 (mRNA,miRNA,DNA甲基化) 来发现新的生物标志物.
  • 建立基因驱动的相关图方法,用于学习分子相互作用.

主要方法:

  • 使用基于关联的图表注意力网络版本2 (GATv2) 进行多omics集成.
  • 采用LASSO回归来实现特征解释性和维度缩小.
  • 进行了分层的10倍交叉验证,以进行可靠的性能评估.

主要成果:

  • 实现了高精度 (98.03%) 的精度 (98.12%),回忆 (97.74%) 和F1得分 (97.87%).
  • 已识别并验证的关键生物标志物:hsa-mir-3936,MTCO1P40和CCND2.2.
  • 路径丰富分析证实了与癌症相关信号的相关性.

结论:

  • 生物GAT-LGG有效地分类LGG亚型,并发现临床相关的生物标志物.
  • 该框架捕捉了生物验证的机制,以提供知情决策.
  • 这种方法为瘤学中的多omics集成提供了一个可扩展的基础.