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

Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.6K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.6K
RNA-seq03:21

RNA-seq

11.7K
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...
11.7K
DNA Microarrays02:34

DNA Microarrays

20.7K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
20.7K
Test for Homogeneity01:23

Test for Homogeneity

2.4K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
2.4K

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

Spatial multi-omics integration by cross-modal graph contrastive learning.

Briefings in bioinformatics·2026
Same author

Age Prediction of Human Based on DNA Methylation by Blood Tissues.

Genes·2021
Same author

Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks.

BMC bioinformatics·2021
Same author

DeepUbi: a deep learning framework for prediction of ubiquitination sites in proteins.

BMC bioinformatics·2019
Same author

A deep learning method to more accurately recall known lysine acetylation sites.

BMC bioinformatics·2019
Same author

Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor.

Genes·2018

相关实验视频

Updated: Jan 15, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

653

不同质的图形对比学习用于整合和对齐空间转录组学数据.

Yang Gui1, Zhaorui Tan2, Yan Xu1

  • 1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.

Briefings in bioinformatics
|October 9, 2025
PubMed
概括
此摘要是机器生成的。

格拉斯集成和对齐多个空间转录组学切片,克服单片分析的局限性. 这种深度图形学习框架从复杂的组织数据中增强了生物洞察力.

关键词:
不同质的图表表示学习学习.多切片对齐对齐方式多切片集成的整合.空间转录学 空间转录学

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

1.2K
Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
16:37

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

13.3K

相关实验视频

Last Updated: Jan 15, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

653
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

1.2K
Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
16:37

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization

Published on: August 5, 2008

13.3K

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:

  • 空间转录学 (ST) 从组织切片提供基因表达和空间数据.
  • 对ST切片的独立分析错过了共同的特征,限制了生物发现.
  • 整合多片段ST数据对于全面理解至关重要.

研究的目的:

  • 开发一种新的框架,GRASS,用于整合和对齐多切片空间转录学数据.
  • 为了利用深度图表表示学习来增强ST数据的分析.
  • 通过分析跨多个组织切片的共同和独特特征来改善生物洞察力.

主要方法:

  • 格拉斯使用了一个深度图表表示学习框架,有两个模块:GRASS_Integration和GRASS_Alignment.
  • GRASS_Integration采用异质图,对比学习和多专家协作进行数据集成.
  • GRASS_Alignment使用双感知相似度指标用于点级对齐和下游任务,如3D重建.

主要成果:

  • 格拉斯在整合和对齐来自五个平台的七个数据集的多片段ST数据方面表现出卓越的性能.
  • 该框架在基准评估中始终超过了八种最先进的方法.
  • 格拉斯有效地捕获共享和独一无二的信息,用于全面的多片分析.

结论:

  • 格拉斯提供了一种有效的解决方案,用于联合分析多切片空间转录学数据.
  • 该框架通过整合和调整跨多个组织切片的数据来增强生物洞察力.
  • 格拉斯代表了空间转录学研究计算工具的重大进步.