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

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
27

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

Beyond Identifier Matching: An Empirical Characterization of Failure Modes in Biomedical Knowledge Graph Integration.

medRxiv : the preprint server for health sciences·2026
Same author

Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePop.

bioRxiv : the preprint server for biology·2026
Same author

Computational strategies for cross-species knowledge transfer.

Nature methods·2025
Same author

Improving Biomedical Knowledge Graph Quality: A Community Approach.

ArXiv·2025
Same author

Examining the genetic links between clusters of immune-mediated diseases and psychiatric disorders.

Translational psychiatry·2025
Same author

Epidural Spinal Arteriovenous Metameric Syndrome (Cobb Syndrome) Causing Thoracic Myelopathy: A Case Report and Review of Literature.

Cureus·2025

相关实验视频

Updated: Jun 4, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.8K

标注公开可用的样本和使用可解释模拟非结构化元数据的研究.

Hao Yuan1,2, Parker Hicks3, Mansooreh Ahmadian4

  • 1Genetics and Genome Sciences Program, Michigan State University, East Lansing, MI 48823, United States.

Briefings in bioinformatics
|December 22, 2024
PubMed
概括

Txt2onto 2.0通过注释非结构化文本到受控的词汇库来增强生物医学数据的重复使用. 这种自然语言处理方法提高了数据的可查性和知识的发现.

关键词:
生物医学元数据数据重复使用.机器学习是机器学习.自然语言处理自然语言处理.

更多相关视频

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.5K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.6K

相关实验视频

Last Updated: Jun 4, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.8K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.5K
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
09:20

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

8.6K

科学领域:

  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 公共可用的生物医学数据庞大,但由于文本描述没有结构,难以重复使用.
  • 生物医学数据的可查性和可重复使用性差,阻碍了科学知识的发现.
  • 对元数据的自动注释对于释放大型生物医学数据集的潜力至关重要.

研究的目的:

  • 引入txt2onto 2.0,这是一个改进的方法,用于注释非结构化的生物医学元数据到受控的词汇.
  • 提高生物医学文本注释的可解释性和性能,特别是在有限的培训数据下.
  • 证明该方法在不同生物医学数据类型和来源中的通用性.

主要方法:

  • Txt2onto 2.0利用自然语言处理和机器学习,使用单词作为功能来提高可解释性.
  • 该方法结合了大型语言模型嵌入来处理未见的单词和解释注释.
  • 该方法在独立数据集上得到验证,包括蛋白质组学和临床试验研究.

主要成果:

  • 与其前身txt2onto 1.0.0相比,txt2onto 2.0显示了更好的性能和可解释性.
  • 该方法准确地预测了各种生物医学研究的疾病注释.
  • 该方法在不同的实验类型和数据源中展示了可通用性.

结论:

  • Txt2onto 2.0 提供了一个强大的解决方案,用于对生物医学文本进行注释,无论数据来源或实验环境如何.
  • 该方法显著提高了公共生物医学数据的可查和可重复使用性.
  • 这一进步有助于从庞大的生物医学数据集中发现更多的知识.