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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.9K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.9K

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

Hematopoietic Rejuvenation via Natural Senolytic NSPCC1 Delays Inflammatory Aging.

Biology·2026
Same author

Comprehensive analysis of interactions between brain aging and late-onset psychoses using heuristic mapping models.

Communications medicine·2026
Same author

Personalized-Context-Aware Age Gap: A New Multi-Omics Measurement Based on Age-Enhanced Model AOE-Net for Aging Acceleration and Chronic Disease Risk Prediction.

Aging cell·2026
Same author

European Bilberry Extract Ameliorates Dietary Advanced Glycation End Products-Induced Non-Alcoholic Steatohepatitis in Rats via Gut Microbiota and Its Metabolites.

Nutrients·2025
Same author

Molecular mechanism of resistance to lonafarnib conferred by mutations in the cysteine-rich region of respiratory syncytial virus fusion glycoprotein and discovery of a lonafarnib-derived antiviral PROTAC.

Journal of virology·2025
Same author

scTFBridge: a disentangled deep generative model informed by TF-motif binding for gene regulation inference in single-cell multi-omics.

Nature communications·2025

相关实验视频

Updated: Jan 17, 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

一种检索增强的知识挖掘方法,具有深入思考的LLM,用于生物医学研究和临床支持.

Yichun Feng1,2,3, Jiawei Wang4, Ruikun He5

  • 1School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, 100049 Beijing, China.

GigaScience
|September 19, 2025
PubMed
概括

本研究引入了一种使用大型语言模型 (LLM) 构建生物医学知识图表并改善问题答案的新方法. 综合和渐进的检索-增强推理 (IP-RAR) 方法提高了信息检索和推理准确度.

关键词:
深刻的思考 深刻的思考 深刻的思考知识图表知识图表知识开采知识开采大型语言模型提取增强生成的提取

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
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

相关实验视频

Last Updated: Jan 17, 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
A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.1K

科学领域:

  • 生物医学信息学 生物医学信息学
  • 人工智能的人工智能
  • 知识表示 知识表示

背景情况:

  • 生物医学知识整合和推理对于科学发现至关重要.
  • 当前的知识图和法学士在处理复杂的术语,数据异质性和快速知识演变方面面临挑战.
  • 在LLM检索和推理中存在限制,以发现跨文档关联.

研究的目的:

  • 开发一个管道构建一个生物医学分层知识图 (BioStrataKG) 使用LLMs.
  • 创建生物医学跨文档问答数据集 (BioCDQA) 以评估知识检索和推理.
  • 引入综合和渐进的检索增强推理 (IP-RAR),以提高检索准确性和推理.

主要方法:

  • 利用LLM从大型生物医疗产品中构建BioStrataKG.
  • 开发了BioCDQA数据集,用于评估潜在知识检索和多节点推理.
  • 实现了IP-RAR,具有集成的基于推理的检索和逐步基于推理的生成与自我反思.

主要成果:

  • IP-RAR显著提高了20%的文件检索F1得分.
  • 与现有方法相比,答案生成准确度提高了25%.
  • 这种方法表现出更好的深度思维和精确的上下文理解.

结论:

  • IP-RAR帮助临床医生将治疗证据整合到个性化药物计划中.
  • 帮助研究人员分析进展并识别研究缺口.
  • 加速科学发现和决策中的假设生成.