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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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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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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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CAS:通过语义丰富来增强隐式受约束数据增强,用于生物医学关系提取和超越.

Fang-Yi Su1, Gia-Han Ngo1, Ben Phan1

  • 1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan City 701401, Taiwan.

Database : the journal of biological databases and curation
|July 3, 2025
PubMed
概括

约束式增强和语义质量 (CAS) 通过使用大型语言模型来生成规则一致的变化来增强受约束数据集的数据增强. 这一框架确保了数据完整性,并改善了生物医学NLP等领域的模型性能.

科学领域:

  • 自然语言处理自然语言处理.
  • 计算生物学 计算生物学
  • 数据科学数据科学数据科学

背景情况:

  • 生物医学关系提取数据集通常具有对数据完整性至关重要的隐性约束.
  • 传统的数据增强方法有可能违反这些特定领域的规则.
  • 现有的技术不足以在受限制的环境中增强数据.

研究的目的:

  • 引入一个新的框架,受约束增强和语义质量 (CAS),用于受约束数据集中的数据增强.
  • 解决传统增强方法在维护数据完整方面的局限性.
  • 改进具有隐性约束的任务上的模型性能.

主要方法:

  • CAS使用大型语言模型来生成各种数据变异.
  • 该框架包含一个用于自我评估和质量控制的SemQ过器.
  • 它确保增强数据遵守预定义的结构,语法或语义规则.

主要成果:

  • CAS成功地生成了高质量,语义一致的增强数据.
  • 该框架保持了结构忠实性和语义准确性.
  • 实验表明使用CAS在多个领域中增强了模型性能.

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结论:

  • 在受约束的数据集中,CAS为数据增强提供了强大的解决方案,特别是在生物医学NLP中.
  • 该框架的多功能性使其适用于具有隐性约束的其他NLP任务.
  • 通过实现可靠的数据增强,同时保持基本数据完整性,CAS在该领域取得了进步.