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

相关实验视频

Updated: Sep 15, 2025

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

13.5K

使用混合深度学习与双核酸可视化融合特征编码的DNA甲基化识别.

Li Tan1, Li Mengshan2, Li Yelin1

  • 1College of Physics and Electronic Information, Gannan Normal University, Ganzhou, 341000, China.

Interdisciplinary sciences, computational life sciences
|July 16, 2025
PubMed
概括

这项研究介绍了DeepDNA-DNVFF,这是一种用于预测DNA甲基化的新方法. 它的高级功能编码和深度学习模型提高了准确性,并提供了对基因调节和疾病生物标志物的洞察.

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

Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition.

NPJ digital medicine·2026
Same author

A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.

BMC biology·2025
Same author

Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.

BMC genomics·2024
Same author

Ensemble learning based on bi-directional gated recurrent unit and convolutional neural network with word embedding module for bioactive peptide prediction.

Food chemistry·2024
Same author

A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.

BMC cancer·2024
Same author

Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.

PLoS computational biology·2023

科学领域:

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

背景情况:

  • 目前用于DNA甲基化预测的机器和深度学习方法在从DNA序列中提取特征方面存在局限性.
  • 现有的模型往往侧重于单一的甲基化类型,缺乏普遍性和稳定性.
  • 需要先进的方法来充分利用序列信息来准确地预测DNA甲基化.

研究的目的:

  • 提出一种新且高效的方法,DeepDNA-DNVFF,用于通用DNA甲基化预测.
  • 开发一种改进的特征编码技术,从DNA序列中提取更多潜在信息.
  • 为了提高预测准确度,并捕捉DNA序列中的远程依赖性.

主要方法:

  • 通过整合2D DNA可视化技术,开发了一种新的双核酸视觉融合特征编码 (DNVFF) 方法.
  • 采用混合深度学习模型,结合了卷积神经网络 (CNN),双向长期短期记忆 (BiLSTM) 和注意力机制.
  • 在多种物种数据集中对传统编码方法和最先进的方法进行了DeepDNA-DNVFF的评估.

主要成果:

  • 与传统方法相比,DNVFF编码方法证明了从DNA序列中提取潜在特征信息的优越性.
  • 在17个物种数据集中的10个中,DeepDNA-DNVFF的表现优于现有的先进方法.

更多相关视频

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

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

37.3K
Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

8.7K

相关实验视频

Last Updated: Sep 15, 2025

Methodology for Accurate Detection of Mitochondrial DNA Methylation
12:11

Methodology for Accurate Detection of Mitochondrial DNA Methylation

Published on: May 20, 2018

13.5K
Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

37.3K
Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

8.7K
  • 达到了最大的马修斯相关系数,大约比最先进的1.24%高,表明预测性能有所改善.

    • 结论:

    • 深DNA-DNVFF为预测DNA甲基化位点提供了一种有效的方法.
    • 该方法为了解基因调节机制提供了宝贵的见解.
    • 这些发现表明,通过精确的DNA甲基化预测来识别疾病生物标志物的潜在应用.