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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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Updated: Jan 13, 2026

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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大型语言模型的应用,用于将基因注释到反应体路径中.

Guanming Wu1, Lisa Matthews2, Nathan Boyer3

  • 1Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, United States.

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PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 通过预测基因功能和提取文献证据,协助策划生物通路知识库Reactome. 这种人工智能驱动的方法提高了效率,并支持了手动策划的努力.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 生命科学中的人工智能

背景情况:

  • Reactome是一个全面的,手动策划的生物通路知识库.
  • 手工策划是劳动密集型的,并且很难跟上生物医学文献的增长.
  • 大型语言模型 (LLM) 和人工智能 (AI) 为生物信息学资源开发提供了潜在的解决方案.

研究的目的:

  • 探索在Reactome手动策划中采用LLM/AI技术.
  • 开发和验证一个LLM工作流程,以协助策展人进行基因注释和路径改进.
  • 评估人工智能生成的摘要和提取的关系对改善Reactome内容的有用性.

主要方法:

  • 开发了一个LLM工作流来预测基因路径关联,并识别支持文献.
  • 从全文论文中生成了潜在分子机制的文本摘要和提取的功能关系.
  • 使用语义相似性比较与现有的Reactome注释和手动策展人评估验证的工作流输出.
  • 增强了基于反应的注释的蛋白质-蛋白质相互作用数据的工作流.

主要成果:

  • 在LLM工作流程中,生成的摘要和Reactome注释之间的高度相似性匹配得到了显著的丰富.
  • 手动评估表明,超过一半的工作流的输出对于支持策划任务是有用的.
  • 结合蛋白质-蛋白质相互作用数据的增强工作流改善了基于反应的注释.

结论:

  • 初步采用LLM/AI技术显示了Reactome策划的令人鼓舞的结果.
  • 开发的工作流提供了一个实用的框架,用于将人工智能辅助方法集成到Reactome策划管道中.
  • 采用的策略可能广泛适用于其他社区知识库.