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

RNA-seq03:21

RNA-seq

10.0K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
10.0K

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

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026
Same author

EnAcrPred: A robust ensemble machine learning framework for identifying anti-CRISPR proteins.

Protein science : a publication of the Protein Society·2026
Same author

DeepOTG: An effective deep learning framework for identifying human protein O-linked threonine glycosylation sites via attention-based information bottleneck.

International journal of biological macromolecules·2026
Same author

Contrastive representation learning and capsule networks enable accurate identification of ferroptosis-related proteins.

Journal of cheminformatics·2026
Same author

GeoCTP: Structure-aware Prediction of Multifunctional Cancer Therapy Peptides via Graph Transformer and Contrastive Learning.

IEEE journal of biomedical and health informatics·2026
Same author

BiToxNet: a deep learning framework integrating multimodal features for accurate identification of neurotoxic peptides and proteins.

BMC biology·2026

相关实验视频

Updated: Jul 1, 2025

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

37.1K

可解释的多层次深度学习用于跨多个物种的RNA甲基化分析.

Rulan Wang1, Chia-Ru Chung2, Tzong-Yi Lee3

  • 1Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

International journal of molecular sciences
|March 13, 2024
PubMed
概括

一个新的深度学习模型准确地预测了跨物种的多种RNA修饰. 这种计算方法识别了潜在的"生物语法"来理解RNA修饰机制.

关键词:
基因组RNA的修饰是RNA的修饰.可以解释的预测预测.基于语言的深度学习模型.多尺度生物信息分析.

更多相关视频

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis
08:50

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

Published on: May 14, 2020

6.6K
Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

25.5K

相关实验视频

Last Updated: Jul 1, 2025

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

37.1K
Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis
08:50

Methylated RNA Immunoprecipitation Assay to Study m5C Modification in Arabidopsis

Published on: May 14, 2020

6.6K
Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

25.5K

科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 分子生物学分子生物学

背景情况:

  • RNA修饰对于细胞调节至关重要,但传统的检测方法效率低下.
  • 现有的技术往往缺乏跨物种的适用性,阻碍了全面的分析.
  • 对于可解释的,多种类型的RNA修饰研究,需要一种多功能计算方法.

研究的目的:

  • 开发一种用于预测各种RNA修饰的新型计算模型.
  • 为了能够对不同物种的RNA修饰进行可解释的,顺序级别的分析.
  • 揭示基底的生物语法和RNA修饰的机制.

主要方法:

  • 设计了一个基于生物语言的多尺度深度学习模型.
  • 该模型在各种RNA修饰数据集上进行了训练和验证.
  • 基准比较和注意力重量可视化用于分析.

主要成果:

  • 拟议的模型在预测各种RNA甲基化类型方面明显优于现有的最先进方法.
  • 跨物种验证证实了该模型的稳定性和通用性.
  • 注意重量分析揭示了模型捕捉功能基因组语义的能力.

结论:

  • 开发的深度学习模型提供了一种优越的,可解释的方法来预测跨物种的RNA修饰.
  • 这些发现表明,存在
  • 生物语法 生物语法 生物语法
  • 它可以绘制甲基化模式并阐明RNA修饰机制.