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

Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
3.5K

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

KLaR: fusing knowledge graphs and language models for biomedical target discovery.

Bioinformatics (Oxford, England)·2026
Same author

Graph-based RNA structural representation reveals determinants of subcellular localization.

Briefings in bioinformatics·2026
Same author

Transcending Structural Dependencies: A Tunable Mass Spectrometry-Driven Machine Learning Framework for Genotoxicity Prediction.

Environmental science & technology·2026
Same author

Intelligent methods in bioinformatics and genomics.

Methods (San Diego, Calif.)·2026
Same author

Learning drug synergy through environment-conditioned feature modulation.

Bioinformatics (Oxford, England)·2026
Same author

A deep adversarial network model for multi-task analysis of single-cell omics data.

Briefings in bioinformatics·2026
Same journal

K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.

Briefings in bioinformatics·2026
Same journal

Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.

Briefings in bioinformatics·2026
Same journal

scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.

Briefings in bioinformatics·2026
Same journal

scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.

Briefings in bioinformatics·2026
Same journal

FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data.

Briefings in bioinformatics·2026
Same journal

METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.

Briefings in bioinformatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 10, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

8.4K

MSlocPRED:基于深度转移学习的多标签mRNA亚细胞定位的识别.

Yun Zuo1, Bangyi Zhang1, Wenying He2

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, No. 1800 Lihu Avenue, Binhu District, Wuxi 214000, China.

Briefings in bioinformatics
|October 14, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了MSlocPRED,这是一种用于预测多标签信使RNA (mRNA) 亚细胞定位的新型计算模型. MSlocPRED显著优于现有的工具,提高了我们对基因表达调节的理解.

关键词:
深度转移学习是指深度转移学习.可以解释的分析分析.序列分析分析的序列分析.亚细胞局部化的局部化

更多相关视频

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments
07:51

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

8.2K
Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays
10:56

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Published on: January 16, 2018

5.8K

相关实验视频

Last Updated: Jun 10, 2025

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells
11:06

Multi-color Localization Microscopy of Single Membrane Proteins in Organelles of Live Mammalian Cells

Published on: June 30, 2018

8.4K
Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments
07:51

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

Published on: December 21, 2017

8.2K
Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays
10:56

Metabolic Labeling and Profiling of Transfer RNAs Using Macroarrays

Published on: January 16, 2018

5.8K

科学领域:

  • 分子生物学分子生物学
  • 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:

  • 传递 RNA (mRNA) 的亚细胞局部化对于调节基因表达至关重要.
  • 对于mRNA本地化预测的现有计算方法通常与多标签注释和概括扎.
  • 为了更深入地了解转化控制,需要对mRNA亚细胞局部化的改进预测.

研究的目的:

  • 开发一种新的计算模型,MSlocPRED,用于预测多标签mRNA亚细胞局部化.
  • 解决现有方法在处理多个本地化注释方面的局限性,并提高预测性能.
  • 为分析大型生物数据集中的mRNA局部化模式提供一个强大的工具.

主要方法:

  • mRNA序列被预处理并转化为图像表示.
  • 一种新的MDNDO-SMDU重新采样技术用于数据集平衡.
  • 深度转移学习被用来构建MSlocPRED模型用于多标签分类.
  • 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).

主要成果:

  • 在两个数据集中,MSlocPRED准确地预测了16和18类的多标签mRNA亚细胞局部化.
  • 拟议的MDNDO-SMDU重新采样技术在数据预处理方面表现出卓越的性能.
  • 在使用来自5'和3'未翻译区域 (NC末端) 的35个核酸时,获得了最佳的预测准确度.
  • 在独立测试和交叉验证方面,MSlocPRED显著优于已有的预测工具.

结论:

  • 在预测多标签mRNA亚细胞局部化方面,MSlocPRED提供了显著的进步.
  • 开发的模型和重新采样技术为分子生物学研究提供了宝贵的工具.
  • 该研究强调了定制预处理和深度学习对于复杂的生物预测的重要性.