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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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精细调整罕见疾病的大型语言模型概念规范化规范化

Andy Wang1,2, Cong Liu2, Jingye Yang3

  • 1Peddie School, Hightstown, NJ, USA.

bioRxiv : the preprint server for biology
|January 18, 2024
PubMed
概括

精细调整Llama 2与人类现象型本体学数据显著改善了罕见疾病概念的正常化. NAME+SYN模型在未见的同义词中实现了超过92%的准确性,超过了ChatGPT-3.5.5.的性能.

关键词:
在 HPO HPO 中.大型语言模型.拉玛2拉玛2拉玛2拉玛2拉玛2拉玛2概念规范化 概念规范化精细调整 精细调整 精细调整

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科学领域:

  • 自然语言处理自然语言处理.
  • 生物信息学是一种生物信息学.
  • 医疗信息学 医疗信息学

背景情况:

  • 罕见疾病概念的规范化对于临床数据分析至关重要.
  • 现有的方法在表型术语表示的变化中扎.

研究的目的:

  • 开发一种新的方法来规范罕见疾病概念,使用精心调整的Llama 2.
  • 提高从临床叙述中识别人类现象型本体学 (HPO) 标识符的准确性.

主要方法:

  • 微调的Llama 2 (Llama2-7B) 具有两个来自HPO的域特定体:HPO名称 (NAME) 和具有同义词的HPO名称 (NAME+SYN).
  • 使用各种表型术语评估模型性能,包括带有字体错误和未见同义词的术语.
  • 将微调模型与ChatGPT-3.5.5对比起来.

主要成果:

  • 微调模型在微调体内存在表型术语时,达到99%以上的准确性.
  • NAME+SYN模型在未见的HPO同义词中显示了92.7%的准确性,显著优于ChatGPT-3.5 (~20%).
  • "NAME+SYN"模型的准确性提高了 (61.8%),并进行了对字体类型的微调.

结论:

  • 精心调整的Llama 2模型可以有效地使表型术语正常化,包括在培训数据中不存在的拼写错误和同义词.
  • 这种方法为使用大型语言模型从临床文本中提取和规范化医疗实体提供了一个强大的解决方案.
  • 该研究强调了特定领域微调的潜力,以提高生物医学应用中的LLM性能.