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

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

Arsenic trioxide depletes cancer stem-like cells and inhibits repopulation of neurosphere derived from glioblastoma by downregulation of Notch pathway.

Toxicology letters·2013
Same author

A prospective, randomized, open-label study comparing the efficacy and safety of preprandial and prandial insulin in combination with acarbose in elderly, insulin-requiring patients with type 2 diabetes mellitus.

Diabetes technology & therapeutics·2013
Same author

Synthesis of the C-18-C-34 fragment of amphidinolides C, C2, and C3.

Organic letters·2013
Same author

Synthesis of the C-1-C-17 fragment of amphidinolides C, C2, C3, and F.

Organic letters·2013
Same author

77Se solid-state NMR of As2Se3, As4Se4 and As4Se3 crystals: a combined experimental and computational study.

Physical chemistry chemical physics : PCCP·2013
Same author

Nanocellulose electroconductive composites.

Nanoscale·2013

相关实验视频

Updated: Jun 14, 2025

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

一种使用机器学习解码多层异质图形转换器编码表示的miRNA扩散协会预测方法.

SiJian Wen1, YinBo Liu1, Guang Yang1

  • 1School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China.

Scientific reports
|September 3, 2024
PubMed
概括

预测微RNA与疾病的关联对健康至关重要. 使用多层异质编码器和XGBoost机器学习解码器的新MHXGMDA方法通过有效保存信息来提高预测准确性.

关键词:
预测米RNA与疾病的关联.多层异质编码器多层异质编码器多视图相似性网络多视图相似性网络一个XGBoost解码器.

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

661
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

相关实验视频

Last Updated: Jun 14, 2025

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
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

661
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

科学领域:

  • 生物化学和分子生物学
  • 计算生物学 计算生物学
  • 遗传学 是一个遗传学.

背景情况:

  • 微RNA (miRNA) 是重要的非编码RNA,调节疾病过程.
  • 准确预测miRNA与疾病的关系对于诊断和治疗至关重要.
  • 现有的模型在预测过程中面临信息保留方面的挑战.

研究的目的:

  • 开发一种新的计算方法,MHXGMDA,用于预测miRNA与疾病的关联.
  • 为了提高编码-解码过程中的信息保存,以提高准确性.
  • 利用多层异质图形结构和机器学习进行可靠的预测.

主要方法:

  • MHXGMDA使用多层异质编码器来生成miRNA和疾病嵌入.
  • 多视图相似度矩阵作为编码器的输入.
  • 一个XGBoost分类器作为机器学习解码器,集成连接层信息.

主要成果:

  • MHXGMDA在两个基准数据集上表现出卓越的性能,用于预测人类miRNA与疾病的关联.
  • 该方法超过了几种领先的预测方法.
  • 使用接收器操作特征曲线下的面积和精度回调曲线下的面积指标验证了性能.

结论:

  • 拟议的MHXGMDA方法有效地预测了miRNA与疾病的关联.
  • 多层异质编码器-机器学习解码器结构增强了信息保存和预测准确性.
  • MHXGMDA为推进miRNA-疾病关联研究提供了一个有前途的工具.