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相关概念视频

Genomic DNA in Prokaryotes00:46

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The genome of most prokaryotic organisms consists of double-stranded DNA organized into one circular chromosome in a region of cytoplasm called the nucleoid. The chromosome is tightly wound, or supercoiled, for efficient storage. Prokaryotes also contain other circular pieces of DNA called plasmids. These plasmids are smaller than the chromosome and often carry genes that confer adaptive functions, such as antibiotic resistance.
Genomic Diversity in Bacteria
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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相关实验视频

Updated: May 28, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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使用基于语言模型的一类支持向量机算法预测细菌宿主范围的等离子体基因组.

Tao Feng1,2, Xirao Chen1, Shufang Wu1

  • 1Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, PR China.

Microbial genomics
|February 11, 2025
PubMed
概括
此摘要是机器生成的。

HRPredict使用一种新的机器学习方法准确预测等离子体宿主范围. 该工具通过克服现有方法的局限性,提高了对基因转移和细菌适应能力的理解.

关键词:
语言模型语言模型机器学习是机器学习.一个级别的SVM.质粒宿主范围的范围.

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Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 质粒宿主范围的预测对于理解基因传播 (耐药性,毒性) 至关重要.
  • 由于数据库注释有限,现有的机器学习工具低估了主机范围.
  • 移动等离子体可以在不同的细菌物种中转移基因.

研究的目的:

  • 开发一种新的方法,HRPredict,用于准确预测等离子体宿主范围.
  • 解决现有工具低估宿主范围的问题.
  • 提供对等离子体宿主适应性的遗传基础的见解.

主要方法:

  • HRPredict使用一个词向量模型来表示等离子体编码的蛋白质.
  • 使用一类支持向量机 (SVM) 模型采用非负样本学习方法.
  • 宿主范围预测在各种分类层次 (家族,属,物种) 进行.

主要成果:

  • 在基准数据集上,HRPredict实现了高性能指标 (AUC,F1得分,回忆,精度,准确度>0.9).
  • 该方法显示,与HOTSPOT和PlasmidHostFinder相比,主机范围覆盖率更高.
  • 与细菌适应性,致病性和生存相关的鉴定基因是等离子体宿主范围的关键决定因素.

结论:

  • HRPredict提供了对等离子体宿主范围的更全面的预测,克服了以前方法的局限性.
  • 这些发现增强了对由等离子体介导的细菌适应机制的理解.
  • 预计HRPredict将推进广泛宿主范围等离子体传播的研究,并从微生物组数据中促进新型等离子体分析.