A diagnostic model for COVID-19 based on proteomics analysis

Alkady, Walaa; El-Bahnasy, Khaled; Gad, Walaa

doi:10.1016/j.compbiomed.2023.107109

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…2(c)–(h) show the ranking results of feature importance of two sets of datasets and the comparison diagram of the first three features. Taking five feature selection methods (Kruskal–Wallis, Chi-Squared, ANOVA, REDFS and MRMR) as reference objects, 27–32 the performance of the feature sorting methods described in this study was compared.…”

Section: Resultsmentioning

confidence: 99%

Cell recognition based on features extracted by AFM and parameter optimization classifiers

Wang,

Yang,

Wang

et al. 2024

Anal. Methods

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Section: Resultsmentioning

confidence: 99%

Cell recognition based on features extracted by AFM and parameter optimization classifiers

Wang,

Yang,

Wang

et al. 2024

Anal. Methods

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“…Consequently, a machine learning‐based approach was employed to develop a model that can improve the accuracy and stability of AD prediction. The KNN algorithm was chosen for analyzing the proteomics data due to its suitability for high‐dimensional data analysis 28,29 . Notably, based on the KNN algorithm, we identified AQR, ZNF587B, and CRP as the three optimal features from a pool of 198 screened proteins.…”

Section: Discussionmentioning

confidence: 99%

“…The KNN algorithm was chosen for analyzing the proteomics data due to its suitability for high‐dimensional data analysis. 28 , 29 Notably, based on the KNN algorithm, we identified AQR, ZNF587B, and CRP as the three optimal features from a pool of 198 screened proteins.…”

Section: Discussionmentioning

confidence: 99%

A multi‐omics study to monitor senescence‐associated secretory phenotypes of Alzheimer's disease

Yang,

Zhou,

Wang

et al. 2024

Ann Clin Transl Neurol

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ObjectiveAlzheimer's disease (AD) is characterized by the progressive degeneration and damage of neurons in the brain. However, developing an accurate diagnostic assay using blood samples remains a challenge in clinic practice. The aim of this study was to explore senescence‐associated secretory phenotypes (SASPs) in peripheral blood using mass spectrometry based multi‐omics approach and to establish diagnostic assays for AD.MethodsThis retrospective study included 88 participants, consisting of 29 AD patients and 59 cognitively normal (CN) individuals. Plasma and serum samples were examined using high‐resolution mass spectrometry to identify proteomic and metabolomic profiles. Receiver operating characteristic (ROC) analysis was employed to screen biomarkers with diagnostic potential. K‐nearest neighbors (KNN) algorithm was utilized to construct a multi‐dimensional model for distinguishing AD from CN.ResultsProteomics analysis revealed upregulation of five plasma proteins in AD, including RNA helicase aquarius (AQR), zinc finger protein 587B (ZNF587B), C‐reactive protein (CRP), fibronectin (FN1), and serum amyloid A‐1 protein (SAA1), indicating their potential for AD classification. Interestingly, KNN‐based three‐dimensional model, comprising AQR, ZNF587B, and CRP, demonstrated its high accuracy in AD recognition, with evaluation possibilities of 0.941, 1.000, and 1.000 for the training, testing, and validation datasets, respectively. Besides, metabolomics analysis suggested elevated levels of serum phenylacetylglutamine (PAGIn) in AD.InterpretationThe multi‐omics outcomes highlighted the significance of the SASPs, specifically AQR, ZNF587B, CRP, and PAGIn, in terms of their potential for diagnosing AD and suggested neuronal aging‐associated pathophysiology.

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Effect and mechanism of Mn2+ on urease activity during anaerobic biological treatment of landfill leachate

Liu,

Huang,

Zhang

et al. 2024

Environ Sci Pollut Res

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A diagnostic model for COVID-19 based on proteomics analysis

Cited by 4 publications

References 33 publications

Cell recognition based on features extracted by AFM and parameter optimization classifiers

Cell recognition based on features extracted by AFM and parameter optimization classifiers

A multi‐omics study to monitor senescence‐associated secretory phenotypes of Alzheimer's disease

Effect and mechanism of Mn2+ on urease activity during anaerobic biological treatment of landfill leachate

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