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

Genomics02:02

Genomics

37.5K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
37.5K
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Neural Circuits01:25

Neural Circuits

1.6K
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...
1.6K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

150
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
150

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 study on the osteogenic effect of CGF membrane combined with bone graft in rabbit calvarial defects.

BMC oral health·2026
Same author

Single cell transcriptome signatures and cell-cell interactions associated with sarcoidosis in lung immune cell populations.

Frontiers in immunology·2026
Same author

BioNeuralNet: a graph neural network based Multi-Omics network data analysis tool.

Bioinformatics (Oxford, England)·2026
Same author

Electronic cigarette aerosols disrupt airway barrier function via MMP-dependent E-cadherin cleavage: findings from cell culture and murine models.

Archives of toxicology·2026
Same author

A RAIR-ATC transcriptional axis and multimodal drug-response modelling reveal class-level vulnerabilities in thyroid cancer.

Frontiers in pharmacology·2026
Same author

An Imported Case of Dengue/Zika Coinfection - Sichuan Province, China, 2026.

China CDC weekly·2026
Same journal

Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data.

ArXiv·2026
Same journal

Overview of the MedHopQA track at BioCreative IX: track description, participation and evaluation of systems for multi-hop medical question answering.

ArXiv·2026
Same journal

Characterizing Universal Object Representations Across Vision Models.

ArXiv·2026
Same journal

CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification.

ArXiv·2026
Same journal

What Do Biomedical NER and Entity Linking Benchmarks Measure? A Corpus-Centric Diagnostic Framework.

ArXiv·2026
Same journal

The Origin of Life in the Light of Evolution.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: Sep 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

生物神经网络:一个基于图形神经网络的多omics网络数据分析工具.

Vicente Ramos1, Sundous Hussein1, Mohamed Abdel-Hafiz1

  • 1Computer Science and Engineering, University of Colorado Denver.

ArXiv
|July 31, 2025
PubMed
概括
此摘要是机器生成的。

生物神经网络是一个新的Python框架,用于使用网络方法分析多omics数据. 它使用图形神经网络 (GNN) 来创建用于精密医学研究的多功能数据嵌入.

更多相关视频

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

相关实验视频

Last Updated: Sep 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.3K

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 系统生物学 系统生物学

背景情况:

  • 多学科数据提供了深刻的生物学见解,但由于高维度和复杂性,存在分析挑战.
  • 基于网络的方法捕捉了分子相互作用,但缺乏用于各种下游分析的综合工具.

研究的目的:

  • 介绍BioNeuralNet,这是一个用于端到端基于网络的多omics数据分析的Python框架.
  • 能够在各种分析任务中有效地利用网络表示.

主要方法:

  • 利用图形神经网络 (GNN) 来学习来自多omics网络的低维表示.
  • 提供一个支持网络构建,嵌入生成和下游分析的模块化框架.

主要成果:

  • 生物神经网络从复杂的分子网络中生成多功能嵌入.
  • 该框架支持多种GNN架构,并与标准Python包集成.

结论:

  • 生物神经网络 (BioNeuralNet) 为多omics网络分析提供了一个灵活,用户友好和开源的解决方案.
  • 通过增强数据解释,促进精准医学中的可重复性研究.