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相关概念视频

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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相关实验视频

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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在线生物医学命名实体通过数据和知识驱动模型的识别.

Lulu Cao1, Chaochen Wu2, Guan Luo3

  • 1Department of Rheumatology and Immunology, Peking University People's Hospital, 100044, China.

Artificial intelligence in medicine
|March 29, 2024
PubMed
概括

这项研究引入了一种新的神经网络,用于识别在非标准化的在线文本中的医疗实体. 该方法通过问答数据和知识标签来增强变压器模型,提高对具有挑战性的生物医学数据的性能.

关键词:
生物医学命名实体的识别.知识表示知识表示.神经网络的神经网络的神经网络在线文本在线文本预培训 预培训 预培训

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科学领域:

  • 生物医学自然语言处理
  • 医疗保健中的人工智能

背景情况:

  • 命名实体识别 (NER) 对于处理生物医学文本至关重要.
  • 现有的NER模型经常因错误和变异而与非标准化的在线医疗文本作斗争.
  • 对非标准化生物医学文本的NER研究仍然有限.

研究的目的:

  • 开发一种神经网络方法,用于在非标准化的在线医疗/健康文本中有效识别实体.
  • 提高变压器模型处理在线生物医学数据的能力.
  • 提高NER模型在杂和多形生物医学文本上的性能.

主要方法:

  • 一种新的神经网络方法,用于识别在非标准的在线医疗/健康文本中的实体.
  • 一个新的预培训计划利用大规模的在线问答对来提高变压器模型的能力.
  • 从知识库中整合多道知识标签,以增强实体表示,克服特定语言的细分挑战.

主要成果:

  • 拟议的神经网络方法在实验中明显优于基线方法.
  • 该模型在中国在线医疗实体识别数据集上取得了最先进的结果.
  • 该方法在处理在线生物医学文本中的错误和多态度方面表现出有效性.

结论:

  • 开发的神经网络方法为未标准化的在线医疗文本中的NER提供了强大的解决方案.
  • 预培训计划和多道知识标签提高了模型性能和通用性.
  • 这项工作推动了生物医学NLP领域的发展,特别是对于现实世界,非标准化数据源.