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

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
Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
MicroRNAs01:22

MicroRNAs

3.0K
MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
3.0K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.3K
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...
13.3K
Neural Regulation01:37

Neural Regulation

39.3K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.3K
Neural Circuits01:25

Neural Circuits

1.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.1K

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 bird's-eye view for future ionic thermoelectrics: from ions to products.

National science review·2026
Same author

Comparison of systolic and diastolic CT-FFR for myocardial ischemia diagnosis.

BMC medical imaging·2026
Same author

Combined carcinoembryonic antigen, carbohydrate antigen 50, and neutrophil gelatinase-associated lipocalin distinguish benign and malignant pleural effusions: a Bayesian analysis.

Scientific reports·2026
Same author

A Classifier Model Based on CT Data from Different CT Phases for Distinguishing LPAs and PCCs.

Archivos espanoles de urologia·2026
Same author

Correction: System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma.

Journal of translational medicine·2026
Same author

An intelligent fusion model for Ki-67 prediction in non-small cell lung cancer: A cloud-based prediction system integrating radiomics.

European journal of radiology·2026

相关实验视频

Updated: Jun 23, 2025

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

1.6K

PGCNMDA:学习节点表示沿路径与图形卷积网络来预测miRNA-疾病关联.

Shuang Chu1, Guihua Duan2, Cheng Yan1

  • 1School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China.

Methods (San Diego, Calif.)
|June 23, 2024
PubMed
概括

我们开发了PGCNMDA,这是一种使用图形卷积网络来预测miRNA-疾病关联的新计算方法. 这种方法提高了准确性,通过识别与各种疾病相关的关键微RNA来帮助诊断和治疗疾病.

关键词:
图表 卷积网络 卷积网络与miRNA疾病的关联路径学习学习路径学习路径空间卷积的空间卷积

更多相关视频

mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

2.5K
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

相关实验视频

Last Updated: Jun 23, 2025

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

1.6K
mirMachine: A One-Stop Shop for Plant miRNA Annotation
06:16

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Published on: May 1, 2021

2.5K
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

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:

  • 识别微RNA-疾病关联 (MDA) 对疾病诊断和治疗至关重要.
  • 用于MDA识别的实验方法是昂贵和耗时的.
  • 计算方法,特别是图形卷积网络 (GCNs),显示出对MDA预测的希望.

研究的目的:

  • 提出一种新的计算方法,PGCNMDA,用于增强推断miRNA-疾病关联.
  • 通过从路径中学习空间运算符来利用GCN来改进MDA预测.
  • 在实际应用中验证PGCNMDA的有效性和可行性.

主要方法:

  • 开发了PGCNMDA,一种使用图形卷积网络 (GCNs) 的方法.
  • 整合了一个学习图形空间运算符,该运算符来自GCN框架内的路径.
  • 在HMDD v2.0和HMDD v3.2数据集上使用5倍交叉验证 (5-CV),10倍交叉验证 (10-CV) 和全局离开一次的交叉验证 (GLOOCV) 评估了PGCNMDA性能.

主要成果:

  • 在HMDD v2.0.0.上,PGCNMDA实现了高性能,AUC约为0.923左右,AUPRC约为0.921左右.
  • 在HMDD v3.2上,PGCNMDA表现出卓越的性能,AUC约为0.941和AUPRC约为0.942.
  • 案例研究证实了高百分比 (高达50/50) 的最高预测miRNA疾病链接胰腺瘤,甲状腺瘤和白血病.

结论:

  • 在预测miRNA与疾病的关联方面,PGCNMDA显著优于现有的方法.
  • 从路径学习空间运算符的新方法提高了GCN对MDA推断的性能.
  • 在疾病诊断和治疗开发中,PGCNMDA显示出强大的实际应用潜力.