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

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

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

Evaluating the Potential Impact of AI on Urinary Tract Infection Diagnosis in the Emergency Department Across Demographic Groups: Retrospective Cohort Study.

JMIR AI·2026
Same author

Humans and Large Language Models in Clinical Decision Support: A Study with Medical Calculators.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

BEnchmarking Large Language Models for Ophthalmology (BELO): An Expert-Curated Data Set and Evaluation Framework for Knowledge and Reasoning.

Ophthalmology science·2026
Same author

Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology.

ArXiv·2025
Same author

MedCalc-Bench: Evaluating Large Language Models for Medical Calculations.

ArXiv·2025
Same author

Language Enhanced Model for Eye (LEME): An Open-Source Ophthalmology-Specific Large Language Model.

ArXiv·2025
Same journal

MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes.

ArXiv·2026
Same journal

A Positron Range Correction with Texture Preservation Framework in PET Imaging.

ArXiv·2026
Same journal

Automated optimization of force field parameters against ensemble-averaged measurements with Bayesian Inference of Conformational Populations.

ArXiv·2026
Same journal

Droplet Fusion as a Relaxation Process: Comparison with Shape Recovery of Newtonian and Viscoelastic Droplets.

ArXiv·2026
Same journal

Ridge-filter crosstalk in conformal proton FLASH planning: dependence on beamlet pitch and iterative mitigation.

ArXiv·2026
Same journal

Electrochemical DNA Hairpin Sensors for Differentiating Small Molecule Intercalation from Minor Groove Binding.

ArXiv·2026
查看所有相关文章

相关实验视频

Updated: Jan 16, 2026

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

1.0K

对生物医学自然语言处理应用程序和建议进行大型语言模型的基准测试.

Qingyu Chen1,2, Yan Hu3, Xueqing Peng1

  • 1Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, USA.

ArXiv
|October 1, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 在生物医学自然语言处理 (BioNLP) 中显示出潜力,但微调传统模型往往表现更好. 封闭源代码的LLM在推理方面表现出色,而开源模型则需要对BioNLP任务进行进一步优化.

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

相关实验视频

Last Updated: Jan 16, 2026

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

1.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K

科学领域:

  • 生物医学自然语言处理 (BioNLP)
  • 医疗保健中的人工智能
  • 计算语言学 计算语言学

背景情况:

  • 生物医学文献的指数增长需要自动化知识提取.
  • 生物医学自然语言处理 (BioNLP) 为高效的信息合成提供了一个解决方案.
  • 大型语言模型 (LLM) 在专门的BioNLP任务中的有效性尚未得到充分证实.

研究的目的:

  • 系统地评估领先的大型语言模型 (LLM) 在各种生物NLP基准上的表现.
  • 为了比较LLM性能 (零拍摄,少数拍摄,微调) 与传统的微调模型,如BERT和BART.
  • 确定实际挑战,并为生物NLP的LLM应用提供见解.

主要方法:

  • 在12个BioNLP基准和6种应用类型中对4个LLM (GPT,LLaMA代表) 的评估.
  • 对LLMs的零射击,少数射击和微调方法的比较分析.
  • 与微调的BERT和BART模型进行基准测试,包括对不一致性,幻觉和成本的分析.

主要成果:

  • 传统的微调模型在大多数BioNLP任务中通常优于零或少射击的LLM.
  • 封闭源代码的LLM (例如,GPT-4) 在推理密集型任务中表现出卓越的表现,比如回答医疗问题.
  • 开源的LLM需要微调才能达到竞争性表现,并且观察到信息遗漏和幻觉等问题.

结论:

  • 微调仍然是BioNLP的强大策略,经常超过基本的LLM提示.
  • 特定的LLM对复杂的推理任务有希望,但需要仔细验证.
  • 需要实用指导方针来解决LLM的局限性,并优化其在生物医学知识处理中的使用.