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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.3K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.3K

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

SARGE: A novel framework for miRNA-mRNA interaction prediction combining jumping knowledge aggregation with multi-layer graph attention network.

International journal of biological macromolecules·2026
Same author

Evaluation of the therapeutic effect of new hypoglycemic drugs on patients with heart failure with reduced ejection fraction and type 2 diabetes: a systematic review and network meta-analysis.

Frontiers in cardiovascular medicine·2026
Same author

scALGSL: Active Learning and Graph Structure Learning for Cell Type Annotation From Single-Cell RNA-seq Data.

IEEE transactions on computational biology and bioinformatics·2026
Same author

An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.

iScience·2026
Same author

scBIT: Integrating Single-Cell Transcriptomic Data Into fMRI-Based Prediction for Alzheimer's Disease Diagnosis.

IEEE transactions on medical imaging·2026
Same author

LMSCDA: A Secondary Structure Enhanced Language Model for Predicting CircRNA and Disease Associations.

IEEE journal of biomedical and health informatics·2026

相关实验视频

Updated: Jan 16, 2026

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

一个多源转换器引导图形表示学习框架,用于circRNA-Disease协会预测.

Si-Zhe Liang1, Lei Wang2,3, Zhu-Hong You4

  • 1School of Electronic Information, Xijing Univerity, Xi'an 710123, China.

ACS omega
|September 29, 2025
PubMed
概括

这项研究介绍了MTGCDA,这是一种用于预测循环RNA疾病关联的新型计算模型. MTGCDA利用多源异质图形变压器实现高精度,有助于早期疾病诊断和向治疗.

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
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

2.0K

相关实验视频

Last Updated: Jan 16, 2026

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
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
In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions
10:27

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

2.0K

科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 循环RNAs (circRNAs) 具有稳定性和组织特异性表达,使它们成为有希望的疾病生物标志物.
  • 预测circRNA疾病关联对于早期诊断和治疗至关重要,但由于复杂的数据和传统方法的局限性而受到挑战.

研究的目的:

  • 开发一个高精度的计算模型,MTGCDA,用于预测circRNA与疾病的关联.
  • 解决信息整合,语义表达和信息丢失在现有预测方法中的局限性.

主要方法:

  • MTGCDA将多源生物信息集成到具有多个节点和边缘类型的异质图中.
  • 代表性学习是使用异质图形神经网络来捕获潜在的语义特征进行的.
  • 一个多层异质图形卷积网络融合了节点嵌入,其次是用于协会评分的CatBoost分类器.

主要成果:

  • 在CircR2Disease数据集上,MTGCDA实现了0.9756的曲线下面面积 (AUC),表现优于现有方法.
  • 20个预测的circRNA疾病关联中,有17个得到了文献报告的验证,证实了模型的准确性和实用性.

结论:

  • MTGCDA模型在预测circRNA与疾病的关联方面表现出卓越的表现.
  • MTGCDA提供了一种实用而准确的计算方法,用于识别潜在的circRNA疾病联系,支持生物标志物发现和治疗策略.