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

Protein Import into the Peroxisomes01:27

Protein Import into the Peroxisomes

3.5K
Cells contain membrane-bound organelles called peroxisomes that oxidize organic molecules by transferring hydrogen atoms to oxygen, producing hydrogen peroxide. Peroxisomes enzymatically convert the released hydrogen peroxide into water and oxygen.
Peroxisomal Protein Import:
Peroxisomes lack the genetic machinery required to code for their own proteins. Hence, most peroxisomal membrane, lumenal and transmembrane proteins are synthesized in the cytoplasm or ER and transported to the peroxisome...
3.5K
Peroxisomes01:24

Peroxisomes

12.6K
Peroxisomes are specialized organelles present in fungi, plant, and animal cells. It can vary in number, size, morphology, and activity depending on the type of tissue and the nutritional state of the cell. For example, cells with active lipid metabolism, such as adipocytes, neurons, and hepatocytes, have more peroxisomes than other cells in the body. Besides their primary role in breaking down complex organic molecules, peroxisomes can also synthesize specific macromolecules and participate in...
12.6K
Multi-pass Transmembrane Proteins and β-barrels01:09

Multi-pass Transmembrane Proteins and β-barrels

5.3K
In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
Multi-pass transmembrane proteins such as...
5.3K
Proteomics01:33

Proteomics

7.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.3K

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

MIFNDRA: an innovative knowledge-enhanced multimodal fusion and graph learning framework for predicting drug resistance-related ncRNAs.

Briefings in bioinformatics·2026
Same author

EZPro-Multi: Contrastive Learning-Enhanced Multi-property Prediction for Enzyme Engineering.

Journal of chemical theory and computation·2026
Same author

Differential digestive fate of palm oil and palm-based diacylglycerol oil: linking physicochemical properties, oxidative stability and pancreatic lipase specificity.

Food chemistry·2026
Same author

Corrigendum to "Chitosan-based bone-targeted nanoparticles delivery of cyclolinopeptide J for the synergistic treatment of osteoporosis" [Int. J. Biol. Macromol. 304 (2025) 140884].

International journal of biological macromolecules·2026
Same author

EMGMDA: a multi-modal graph neural framework for robust prediction of miRNA-disease associations.

BMC genomics·2026
Same author

Turnover of NESTIN-Negative Neural Progenitors Into NESTIN-Positive State by the Lack of JMJD3.

Genes to cells : devoted to molecular & cellular mechanisms·2026

相关实验视频

Updated: Jul 6, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

ProSE-Pero:基于自主监督的多任务语言预训练模型的氧体蛋白局部识别模型.

Jianan Sui1, Jiazi Chen2, Yuehui Chen3

  • 1School of Information Science and Engineering, University of Jinan, 250022 Jinan, Shandong, China.

Frontiers in bioscience (Landmark edition)
|January 5, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了ProSE-Pero模型,用于准确识别和定位Peroxisomal蛋白质. 这种深度学习方法显著改进了现有的方法,有助于疾病研究.

关键词:
这就是SVMSMOTE.深度学习是一种深度学习.功能选择 功能选择多任务语言模型多任务语言模型氧体局部化识别识别在真空中,真空蛋白质的识别蛋白质.

更多相关视频

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

8.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405

相关实验视频

Last Updated: Jul 6, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

8.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405

科学领域:

  • 生物化学和分子生物学
  • 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:

  • 过氧体是含有氧化酶的重要器官.
  • 错误折叠的过氧体蛋白与各种疾病有关.
  • 精确识别和定位过氧体蛋白对于理解细胞功能和疾病机制至关重要.

研究的目的:

  • 开发一种高度准确的模型,用于识别和定位过氧体蛋白质.
  • 利用深度表示学习从蛋白质序列中提取特征.
  • 建立一个强大的计算工具,以推进氧体研究.

主要方法:

  • 采用深度表示学习模型来提取多氧体蛋白特征.
  • 使用SVMSMOTE,SHAP,ANOVA和LightGBM来进行特征选择和比较.
  • 训练并验证了ProSE-Pero模型,使用十倍的交叉验证对160个过氧体蛋白的数据集进行验证.

主要成果:

  • ProSE-Pero模型实现了高性能:93.37%的特异性,82.41%的灵敏性,95.77%的准确性和0.9818的AUC.
  • 成功扩展了该方法,以91.90%的准确度识别植物真空蛋白,优于iPVP-DRLF模型.
  • 与In-Pero模型相比,在过氧体蛋白位址和识别方面表现出优异的性能.

结论:

  • ProSE-Pero模型在过氧体蛋白的识别和定位方面取得了重大进展.
  • 该研究强调了ProSE语言模型在蛋白质序列特征提取方面的有效性.
  • 该模型的概括能力表明,在线粒体和戈尔吉器官等其他器官中识别蛋白质的潜在应用.