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相关概念视频

Encoding01:19

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Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
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Enzyme-linked receptors are cell-surface receptors acting as an enzyme or associating with an enzyme intracellularly. They make excellent drug targets. Drugs can bind to the extracellular ligand-binding domain or directly affect their enzymatic domain and alter their activity.
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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

Updated: Sep 11, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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有没有LLMs超越生物医学NER的编码器?

Motasem S Obeidat1, Md Sultan Al Nahian2, Ramakanth Kavuluru2

  • 1Department of Computer Science, University of Kentucky, Lexington, KY USA.

Proceedings. IEEE International Conference on Healthcare Informatics
|August 11, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 在生物医学命名实体识别 (NER) 中表现得更好,特别是对于较长的实体. 然而,传统的编码器模型在实时应用中仍然更高效.

关键词:
编码器模型编码器模型大型语言模型.命名实体的认可 命名实体的认可

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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.
  • 计算语言学 计算语言学

背景情况:

  • 生物医学命名实体识别 (NER) 对于信息提取至关重要.
  • 基于变压器的编码器模型 (例如BERT) 是NER的当前标准.
  • 大型语言模型 (LLM) 正在信息提取中出现,但可能会忽略位置信息,并且在计算上昂贵.

研究的目的:

  • 与编码器模型相比,评估生物医学NER的LLM的性能和效率.
  • 评估LLMs的性能增长和计算成本之间的权衡.
  • 通过BIO标记方案调查实体长度对NER绩效的影响.

主要方法:

  • 利用了五个不同的生物医学数据集,其中较长实体的比例不同.
  • 采用BIO (开始,内部,外部) 实体标记方案来保留位置信息.
  • 将LLM (Mistral,Llama 8B) 与最先进的编码器模型 (BERT,BiomedBERT,DeBERTav3 300M) 进行比较.

主要成果:

  • 在大多数数据集上,LLM在F-score方面表现比编码器模型优于2-8%.
  • 较长实体 (≥3个令牌) 的性能增长更为显著.
  • 在LLM中,推断倍数比编码器模型高一到两倍.

结论:

  • 在生物医学NER中,LLM提供了卓越的性能,特别是在复杂的实体中.
  • 当计算效率和实时处理至关重要时,编码器模型可能更适合.
  • 在LLM和编码器模型之间的选择取决于特定的应用要求和资源限制.