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相关概念视频

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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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.
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相关实验视频

Updated: Jan 9, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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使用大型语言模型从患者衍生癌症模型的科学文本中提取知识:算法开发和验证研究研究.

Jiarui Yao1,2, Zinaida Perova3, Tushar Mandloi3

  • 1Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Boston, MA, United States, 1 7813545014.

JMIR bioinformatics and biotechnology
|December 4, 2025
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概括

软提示显著提高了开放式大语言模型 (LLM) 的性能,用于从科学文本中提取患者衍生癌症模型 (PDCM) 实体,与专有模型竞争.

关键词:
在上下文学习学习.提取信息 提取信息提取知识 提取知识大型语言模型.来自患者的癌症模型.快速调整调整的提示软提示提示提示软提示提示

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 在瘤学中使用人工智能
  • 计算生物学 计算生物学

背景情况:

  • 患者衍生癌症模型 (PDCMs) 对于癌症研究和临床前研究至关重要.
  • 与PDCM相关的出版物数量急剧增加,需要有效的知识提取.
  • 大型语言模型 (LLM) 提供了在规模上处理科学文献的高级功能.

研究的目的:

  • 研究基于LLM的系统,用于自动提取与PDCM相关的实体.
  • 为了比较实体提取的直接提示和软提示技术.

主要方法:

  • 探索了直接提示 (手动提示设计) 和软提示 (可训练的连续向量).
  • 在专有 (GPT4-o) 和开放 (LLaMA3) LLMs中评估了这两种方法.
  • 使用了100个PDCM摘要的手动注释数据集,其中包含15个实体类型.

主要成果:

  • 通过直接提示的GPT4-o实现了F1得分50.48 (精确匹配) 和71.36 (重叠匹配).
  • 与直接提示相比,LLaMA3软提示显著提高了性能 (准确匹配:7.06至46.68;重叠匹配:12.0至71.80).
  • 在重叠匹配设置中,LLaMA3软提示略高于GPT4-o直接提示.

结论:

  • 软提示可以提高较小的开放LLM的性能,用于PDCM实体提取.
  • 在开放模型上训练软提示可以产生与专有LLM相比的性能.
  • 这种方法在PDCM研究中促进了可扩展的知识发现.