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

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...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...

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

Improving the design and analytic methods used in NIH-funded clinical trials involving behavioral interventions.

Annals of behavioral medicine : a publication of the Society of Behavioral Medicine·2025
Same author

Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs.

Statistics in medicine·2024
Same author

Design and Analytic Methods to Evaluate Multilevel Interventions to Reduce Health Disparities: Rigorous Methods Are Available.

Prevention science : the official journal of the Society for Prevention Research·2024
Same author

Influential methods reports for group-randomized trials and related designs.

Clinical trials (London, England)·2022
Same author

Benchmarking Effectiveness and Efficiency of Deep Learning Models for Semantic Textual Similarity in the Clinical Domain: Validation Study.

JMIR medical informatics·2021
Same author

Prevention Research at the National Institutes of Health.

Public health reports (Washington, D.C. : 1974)·2017

相关实验视频

Updated: May 29, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.8K

基于变压器的语言模型用于生物医学文献中的群体随机试验分类:模型开发和验证.

Elaheh Aghaarabi1, David Murray1

  • 1Office of Disease Prevention, National Institutes of Health, 6705 Rockledge Dr, Bethesda, MD, 20892, United States, 1 3014964000.

JMIR medical informatics
|May 9, 2025
PubMed
概括

一个新的AI模型准确地识别复杂的临床试验设计,包括组或集群随机试验 (GRTs) 和阶梯组或集群随机试验 (SWGRTs),帮助公共卫生研究. 这个工具增强了专业研究出版物的发现.

关键词:
在这里,我们可以看到AIAIAI.人工智能的人工智能是人工智能.生物医学 生物医学临床试验是指临床试验中的临床试验.数据集数据集数据集发展发展发展发展发展.文件分类 文档分类 文档分类语言模型语言模型机器学习是机器学习.模型模型模型模型模型模型自然语言处理自然语言处理.公共卫生公共卫生.随机化试验是一种随机化试验.工具 工具 工具 工具变压器的变压器是一个变压器.试验试验试验试验试验试验试验

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

655
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

516

相关实验视频

Last Updated: May 29, 2026

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.8K
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

655
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

516

科学领域:

  • 生物医学信息学是生物医学信息学.
  • 公共卫生研究 公共卫生研究
  • 医学中的人工智能

背景情况:

  • 监测公共卫生文献至关重要,但具有挑战性.
  • 鉴定特定的研究设计,如组或集群随机试验 (GRTs),是困难的当前的方法.

研究的目的:

  • 开发一个微调的语言模型,以识别具有特定临床试验设计的出版物.
  • 准确地分类使用组或集群随机试验 (GRT),个人随机组治疗试验 (IRGT) 或阶段曲组或集群随机试验 (SWGRT) 设计的研究.

主要方法:

  • 微调了国家卫生研究院生物医学文献的BioMedBERT语言模型.
  • 训练模型将出版物分为三个嵌套的临床试验设计类别.
  • 对未见的数据进行评估的模型性能,对灵敏度和特异性进行评估.

主要成果:

  • 该模型在所有测试类别中实现了高灵敏度和特异性.
  • 绩效指标包括:负值 (0.95,0.93),GRTs (0.94,0.90),IRGTs (0.81,0.97) 和SWGRTs (0.96,0.99).这些指标包括:负值 (0.95,0.93),GRTs (0.94,0.90),IRGTs (0.81,0.97) 和SWGRTs (0.96,0.99).

结论:

  • 精心调整的,特定领域的语言模型可以准确地识别复杂的研究设计.
  • 该模型为公共卫生界提供了一种有价值的工具,帮助他们找到相关的研究.
  • 解决了对专门的公共卫生研究设计有效识别的关键需求.