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相关概念视频

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Overview
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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基于生物医学文献的临床现象型定义发现,使用大型语言模型进行发现.

Samar Binkheder1,2, Xiaofu Liu2, Michael Wu3

  • 1Medical Informatics Unit, Department of Medical Education, College of Medicine, King Saud University, Riyadh 12372, Saudi Arabia.

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概括

本研究引入了一种自动化文本挖掘方法,从生物医学文献中提取临床表型定义. 开发的临床表型知识库 (CliPheKB) 帮助研究人员有效地发现与表型相关的句子.

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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:

  • 电子健康记录 (EHR) 的表型是至关重要的,但由于未定义的表型而具有挑战性.
  • 需要自动化方法来有效地从广泛的生物医学文献中提取临床表型定义.

研究的目的:

  • 开发和评估一个文本挖掘管道,自动从生物医学文献中提取临床表型定义相关的句子.
  • 创建一个可搜索的临床表型定义知识库.

主要方法:

  • 开发了使用机器学习算法的抽象级和全文句子级分类器,包括支持矢量机 (SVM),物流回归 (LR),天真贝叶斯,决策树,卷积神经网络 (CNN),变压器双向编码器表示 (BERT) 和BioBERT.
  • 使用F-measure比较分类器性能,选择SVM用于抽象级别和BioBERT用于句子级别的分类.
  • 进行了PubMed数据库的大规模选,以确定数百万个相关句子,并构建了临床表型知识库 (CliPheKB).

主要成果:

  • SVM抽象级分类器实现了98%的F测量,识别了超过450万个相关摘要.
  • 生物BERT全文句级分类器实现了91%的F-measure.
  • 该系统成功地提取了超过200万个与临床表型相关的句子,形成了CliPheKB的基础.

结论:

  • 开发的文本挖掘管道为临床表型发现提供了高吞吐量,可泛化和可扩展的方法.
  • 临床表型知识库 (CliPheKB) 为研究人员提供了一个宝贵的资源,可以查询和从生物医学文献中检索表型特定的句子.
  • 这种方法补充了现有的EHR表型化方法,推进了自动化临床研究领域.