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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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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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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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PLRTE:使用大型语言模型进行生物医学关系三重提取的渐进式学习.

Yi-Kai Zheng1, Bi Zeng2, Yi-Chun Feng3

  • 1School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510000, China; Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou 510005, China.

Journal of biomedical informatics
|October 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种渐进式学习策略 (PLRTE),以改进生物医学关系三重提取模型. 这种新的方法增强了药物发现和知识图表构建的模型概括性.

关键词:
生物医学文本挖掘技术大型语言模型.命名实体认可 命名实体认可自然语言处理自然语言处理.关系三重提取关系三重提取有监督的微调.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.
  • 计算生物学 计算生物学

背景情况:

  • 文件级关系三重提取对于生物医学知识的发现至关重要.
  • 目前的模型难以将其推广到新的数据集和关系类型.

研究的目的:

  • 提高生物医学关系三重提取模型的概括能力.
  • 通过专注于数据-任务相关性和关系细节性来优化模型.

主要方法:

  • 引入了一种新的渐进式学习策略,以开发PLRTE模型.
  • 实施了四个级别的渐进式学习过程:语义关系增强,构成指令和双轴级别学习.

主要成果:

  • 在DDI和BC5CDR数据集上,与最新的基线相比,实现了5%至20%的性能改善.
  • 在未曾见过的Chemprot和GDA数据集上展示了特殊的概括性.

结论:

  • 优化数据-任务关联和关系细分度大大提高了模型的通用性.
  • PLRTE模型显示了推动生物医学文本挖掘应用的巨大潜力.