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相关概念视频

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
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The deep ocean and its underlying sediments represent vast, largely unexplored microbial habitats that extend far beyond the sunlit photic zone. The photic (euphotic) zone typically spans the upper ~100–200 meters of pelagic waters in the open ocean, but its depth varies geographically and seasonally, where sufficient light supports photosynthetic life. Below this lies the deep sea, spanning roughly 1000–6000 meters (bathypelagic to abyssal zones), with deeper hadal trenches extending beyond...
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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

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Updated: May 12, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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一个基于微生物知识图的深度学习模型,用于预测目标宿主候选微生物.

Jie Pan1, Zhen Zhang1, Ying Li1

  • 1Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi'an 710069, China.

Briefings in bioinformatics
|March 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了KGVHI,一种使用异质微生物网络和深度学习来预测微生物与宿主相互作用 (MHI) 的新型计算模型. KGVHI准确地识别了宿主潜在的微生物病原体,有助于理解微生物生态和开发向疗法.

关键词:
生物信息学是一种生物信息学.深度学习是一种深度学习.不同质的微生物网络 (HMN)知识图 (KG) 是一个知识图.微生物与宿主相互作用 (MHI)

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科学领域:

  • 微生物生态学 微生物生态学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 预测微生物与宿主相互作用 (MHI) 对于理解微生物组动态,微生物进化和全球健康至关重要.
  • 描述复杂的微生物宿主信号机制是一个重大挑战.
  • 计算方法为确定MHI的实验方法提供了具有成本效益的替代方案.

研究的目的:

  • 开发一种新的计算模型,用于预测与特定宿主相互作用的候选微生物.
  • 利用异质微生物网络 (HMN) 和知识图嵌入用于MHI预测.
  • 提高对微生物与宿主关系的基本监管机制的理解.

主要方法:

  • 构建了一个异构的微生物网络 (HMN),集成人类蛋白质,病毒和病原细菌及其属性.
  • 采用知识图嵌入策略来捕捉全球网络拓.
  • 利用自然语言处理 (NLP) 来提取本地生物属性信息.
  • 将本地和全球信息集成到一个混合深度神经网络 (DNN) 中,用于预测.

主要成果:

  • 在三个MHI数据集上,KGVHI模型取得了卓越的性能,超过了最先进的方法.
  • 涉及致病细菌的案例研究表明,KGVHI对潜在的MHI对有很强的预测能力.
  • 该模型有效地结合了网络结构和生物属性,用于准确的MHI预测.

结论:

  • KGVHI提供了一种强大的计算工具,用于预测微生物与宿主之间的相互作用.
  • 这些发现有助于深入了解微生物生态和开发针对微生物感染的向疗法.
  • 这种方法有助于系统地描述微生物与宿主之间的信号传递.