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

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
Cluster Sampling Method01:20

Cluster Sampling Method

14.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
14.0K
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
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Next-generation Sequencing03:00

Next-generation Sequencing

97.7K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
97.7K

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

No-boundary thinking for artificial intelligence in bioinformatics and education.

Frontiers in bioinformatics·2024
Same author

Formal autopoiesis: Solutions of the classical and extended functional closure equations.

Bio Systems·2023
Same author

System Integrated Information.

Entropy (Basel, Switzerland)·2023
Same author

Identification of potential antiviral compounds against SARS-CoV-2 structural and non structural protein targets: A pharmacoinformatics study of the CAS COVID-19 dataset.

Computers in biology and medicine·2021
Same author

Proceedings of the 2017 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.

BMC bioinformatics·2018
Same author

Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis.

BMC bioinformatics·2017

相关实验视频

Updated: Jan 14, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K

使用自然语言处理,Word2Vec和KMeans优化CDR3序列的集群.

Sanskriti Baranwal1, Ricardo Avila Sanchez2, Clement-Andi Edet1

  • 1University of Dallas, Computer Science Department, Irving, TX, United States.

Frontiers in bioinformatics
|October 20, 2025
PubMed
概括

这项研究引入了一种新的自然语言处理管道来分析T细胞受体 (TCR) CDR3序列. 该方法揭示了急性呼吸困扰综合征 (ARDS) 患者的独特免疫特征,有助于发现生物标志物.

关键词:
) 在此之前,我们已经看到了.生物NLP公司 BioNLP在Word2vec中使用.急性呼吸道疾病综合征 (ARDS)生物信息学和计算生物学没有监督的学习学习.

更多相关视频

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

577

相关实验视频

Last Updated: Jan 14, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.3K
ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.9K
A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

577

科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • T细胞受体 (TCR) 测序对于理解适应性免疫至关重要.
  • 分析互补性决定区域3 (CDR3) 序列的巨大多样性是一个重大挑战.
  • 急性呼吸困扰综合征 (ARDS) 涉及肺部复杂的免疫反应.

研究的目的:

  • 开发和验证一种基于自然语言处理 (NLP) 的新型管道,用于集群TCRβ链CDR3序列.
  • 研究健康对照组,ARDS患者和非ARDS个体之间的CDR3谱结构差异.
  • 探索这种NLP框架在重症监护中用于免疫监测和生物标志物发现的潜力.

主要方法:

  • 使用Word2Vec嵌入来表示CDR3序列.
  • 应用主要组件分析 (PCA) 用于缩小维度.
  • 采用KMeans集群来分析不同患者队列的TCR曲目结构.

主要成果:

  • 减小维度揭示了CDR3序列空间中不同的结构拓.
  • 健康对照显示紧密,低多样性的集群,表明一个稳定的曲目.
  • 患有ARDS的患者表现出分散的,众多的扩散集群,表明免疫谱系的破坏.
  • 非ARDS样本显示中间的剧集组织.

结论:

  • 免疫激活状态反映在CDR3序列空间的结构拓中.
  • 在NLP的管道有效地捕捉了TCR剧目中隐藏的模式.
  • 这种方法提供了一种可扩展的方法,用于在重症监护机构发现生物标志物和免疫监测.
  • 这项研究强调了NLP在免疫学数据分析中对个性化诊断的有用性.