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In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
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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...
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使用大型语言模型进行几次生物医学命名实体识别的提醒

Yao Ge, Sudeshna Das, Yuting Guo

    Research square
    |September 5, 2025
    PubMed
    概括

    带有提取增强生成的动态提示显著提高了大型语言模型 (LLM) 中的少数镜头生物医学命名实体识别 (NER) 性能. 这种适应性策略提高了有限数据的准确性,改善了NER任务的结果.

    科学领域:

    • 自然语言处理 (NLP)
    • 生物医学信息学

    背景情况:

    • 生物医学命名实体识别 (NER) 对于从生物医学文本中提取信息至关重要.
    • 大型语言模型 (LLM) 显示了NER的潜力,特别是在数据有限的少数场景中.

    研究的目的:

    • 提高生物医学NER的LLM性能.
    • 研究动态提示策略的有效性,特别是提取增强生成 (RAG).

    主要方法:

    • 实施并比较LLM的静态和动态提示工程技术.
    • 使用提取增强生成 (RAG) 与TF-IDF和SBERT进行动态样本选择.
    • 在5次和10次射击设置中评估了5个生物医学NER数据集的性能.

    主要成果:

    • 通过结构化组件进行静态提示,GPT-4,GPT-3.5和LLaMA 3-70B的F1分数提高了11-12%.
    • 动态提示进一步提高了性能,TF-IDF和SBERT检索的平均F1得分分别提高了7.3%和5.6%.
    • 动态RAG在一些生物医学NER任务中表现出卓越的结果.

    结论:

    • 通过RAG进行情境适应提示对于改善少量生物医学NER非常有效.

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  • 动态提示策略在基于LLM的生物医学NLP任务中比静态方法具有显著的进步.