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相关概念视频

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
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Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

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一个基于蛋白质组学分析的COVID-19诊断模型.

Walaa Alkady1, Khaled ElBahnasy2, Walaa Gad2

  • 1Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Computers in biology and medicine
|June 5, 2023
PubMed
概括

这项研究引入了使用蛋白质和代谢物进行早期COVID-19检测和严重性预测的新模型. 该模型实现了93%的准确性,识别了与免疫和呼吸系统相关的关键生物标志物.

科学领域:

  • 发现生物标志物的发现.
  • 机器学习在医疗保健中的应用
  • 传染病诊断 传染病诊断 传染病诊断

背景情况:

  • 2019年新冠肺炎疾病 (COVID-19) 的早期诊断对于及时治疗和改善患者结果至关重要.
  • 识别感染病例和预测疾病严重程度是管理大流行病的关键挑战.

研究的目的:

  • 开发和评估用于检测COVID-19感染和确定疾病严重程度的预测模型.
  • 利用蛋白质和代谢物作为准确COVID-19分类的特征.

主要方法:

  • 使用了包括主要组件分析 (PCA),信息获取 (IG) 和差异分析 (ANOVA) 在内的特征选择技术.
  • 三种机器学习分类器 (K-最近邻居,支持矢量机,随机森林) 用于预测.
  • 用准确性,灵敏性,特异性和精度来评估模型性能.

主要成果:

  • 随机森林 (RF) 分类器与ANOVA特征选择实现了92%的准确性.
  • 使用十个选定特征的射频分类器 (7种蛋白质,3种代谢物) 获得了93%的最高准确度.
  • 发现一些特征与免疫和呼吸系统有关.

结论:

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  • 拟议的模型在预测COVID-19感染和严重程度方面显示出有希望的结果.
  • 有效的特征选择和机器学习分类器可以显著提高诊断准确性.
  • 已识别的生物标志物提供了对COVID-19对生理系统影响的见解.