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

Cancer Survival Analysis01:21

Cancer Survival Analysis

357
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
357
Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

8.9K
Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
8.9K
lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

8.6K
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
8.6K
The Retinoblastoma Gene01:20

The Retinoblastoma Gene

4.1K
Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
The first-ever tumor suppressor gene called Rb was identified in retinoblastoma - a rare eye tumor in children. In inherited forms of the disease, a child inherits one defective copy of the Rb gene, which predisposes them to retinoblastoma. However,...
4.1K

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

Clinical and Radiographic Outcomes at a Mean of 7 Years Following Primary Transfibular Total Ankle Arthroplasty in Younger and Older Patients.

The Journal of bone and joint surgery. American volume·2026
Same author

IVCM-Insight: automated interactive interpretation of in vivo confocal microscopy.

NPJ digital medicine·2026
Same author

Corrigendum to "Dual-functional hydrogel platform suppresses M1 activation and stabilizes M2 macrophages in intervertebral disc degeneration" [Mater. Today Bio, 38 (2026), 103087].

Materials today. Bio·2026
Same author

Five views on the foundations of data science.

Patterns (New York, N.Y.)·2026
Same author

2 Parallel Heart: Parallel Experiments of a Highly Accurate Dual Independent Neural Network for Predicting Myocardial Infarction in Service of Clinical Practice.

IEEE transactions on bio-medical engineering·2026
Same author

Research Progress on Nano-TiO<sub>2</sub> Photocatalytic Degradation of Automobile Exhaust.

Molecules (Basel, Switzerland)·2026

相关实验视频

Updated: Jul 12, 2025

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

可解释的多层图形神经网络用于癌症基因预测.

Michail Chatzianastasis1, Michalis Vazirgiannis1, Zijun Zhang2

  • 1LIX, École Polytechnique, IP Paris, Rte de Saclay, Palaiseau, 91120, France.

Bioinformatics (Oxford, England)
|October 20, 2023
PubMed
概括

可解释的多层图形神经网络 (EMGNN) 通过整合多个基因相互作用网络和多omics数据来识别癌症基因. 这种方法提高了预测准确度,并提供了生物学见解,超过了癌症基因组学研究中的现有方法.

科学领域:

  • 计算生物学是一种计算生物学.
  • 癌症基因组学 癌症基因组学
  • 网络科学 网络科学

背景情况:

  • 鉴定癌症基因在癌症基因组学中至关重要但具有挑战性.
  • 像深图神经网络这样的现有方法与多层基因相互作用作斗争,缺乏可解释性.
  • 单个生物网络无法捕捉瘤发生的复杂性,导致不一致的预测.

研究的目的:

  • 开发一个可解释的多层图形神经网络 (EMGNN),用于准确的癌症基因识别.
  • 为了利用多个基因-基因相互作用网络和泛癌多基因数据.
  • 为已识别的癌症基因提供可解释的预测和生物见解.

主要方法:

  • 使用多层图形神经网络架构 (EMGNN).
  • 集成的多个基因-基因相互作用网络和泛癌多基因数据.
  • 纳入了模型级特征重要性和分子级基因组丰富分析以解释性.

主要成果:

  • 与最先进的方法相比,EMGNN在精度回忆曲线下的面积平均提高了7.15%.
  • 成功优先预测新预测的癌症基因与冲突的单个网络预测.
  • 通过可解释的AI技术提供了宝贵的生物学见解.

更多相关视频

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

1.7K
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

792

相关实验视频

Last Updated: Jul 12, 2025

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
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

1.7K
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

792

结论:

  • EMGNN为癌症基因识别提供了一种新的图形学习范式.
  • 该方法有效地模拟了多层的拓基因关系.
  • EMGNN是促进癌症基因组学研究的宝贵工具.