ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.

<p><b>A.</b> ROC curve view shows an area under ROC curve (AUC) of 0.962. <b>B.</b> Tester analysis shows 93% (70 out of 75) of correct classification. ROC, Receptor operating characteristics.</p>

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Autor principal: Ricardo E. Correa Fierro (22676430) (author)
Outros Autores: Noroska Gabriela Mogollón Salazar (22676433) (author), Washington B. Cárdenas (3213597) (author), Evencio Joel Medina-Villamizar (22676436) (author), Jefferson Pastuña-Fasso (22676439) (author), Melanie Ochoa-Ocampo (21222826) (author), Giovanna Morán-Marcillo (22676442) (author), Mary Ernestina Regato Arrata (22676445) (author), Mildred Zambrano (16641645) (author), Joyce Andrade (16641648) (author), Juan Chang (8280894) (author), Saurabh Mehta (367781) (author), Fernanda Bertuccez Cordeiro (16641642) (author)
Publicado em: 2025
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_version_ 1849927643272577024
author Ricardo E. Correa Fierro (22676430)
author2 Noroska Gabriela Mogollón Salazar (22676433)
Washington B. Cárdenas (3213597)
Evencio Joel Medina-Villamizar (22676436)
Jefferson Pastuña-Fasso (22676439)
Melanie Ochoa-Ocampo (21222826)
Giovanna Morán-Marcillo (22676442)
Mary Ernestina Regato Arrata (22676445)
Mildred Zambrano (16641645)
Joyce Andrade (16641648)
Juan Chang (8280894)
Saurabh Mehta (367781)
Fernanda Bertuccez Cordeiro (16641642)
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author_facet Ricardo E. Correa Fierro (22676430)
Noroska Gabriela Mogollón Salazar (22676433)
Washington B. Cárdenas (3213597)
Evencio Joel Medina-Villamizar (22676436)
Jefferson Pastuña-Fasso (22676439)
Melanie Ochoa-Ocampo (21222826)
Giovanna Morán-Marcillo (22676442)
Mary Ernestina Regato Arrata (22676445)
Mildred Zambrano (16641645)
Joyce Andrade (16641648)
Juan Chang (8280894)
Saurabh Mehta (367781)
Fernanda Bertuccez Cordeiro (16641642)
author_role author
dc.creator.none.fl_str_mv Ricardo E. Correa Fierro (22676430)
Noroska Gabriela Mogollón Salazar (22676433)
Washington B. Cárdenas (3213597)
Evencio Joel Medina-Villamizar (22676436)
Jefferson Pastuña-Fasso (22676439)
Melanie Ochoa-Ocampo (21222826)
Giovanna Morán-Marcillo (22676442)
Mary Ernestina Regato Arrata (22676445)
Mildred Zambrano (16641645)
Joyce Andrade (16641648)
Juan Chang (8280894)
Saurabh Mehta (367781)
Fernanda Bertuccez Cordeiro (16641642)
dc.date.none.fl_str_mv 2025-11-24T18:25:19Z
dc.identifier.none.fl_str_mv 10.1371/journal.pntd.0013691.g004
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/ROC_curve_analysis_of_a_set_of_biomarkers_obtained_via_untargeted_metabolomics_in_serum_samples_from_children_infected_with_DENV_compared_to_Control_/30696720
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Biochemistry
Medicine
Microbiology
Cell Biology
Biotechnology
Cancer
Virology
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
receiver operating characteristic
highest predictive power
faster patient screening
component 5 showing
complex immune response
serum lipid metabolome
evaluate biomarker performance
denv ), triggers
serum lipidomics profiling
new diagnostic tools
div >< p
disproportionately affects children
assess group separation
pediatric dengue fever
dengue virus infection
denv infected group
dengue virus
dengue fever
lipid metabolism
diagnostic methods
diagnosis tools
dengue research
biomarker potential
biomarker discovery
z </
subtropical regions
metabolic alterations
may contribute
mass spectrometry
key role
glycerol lipids
fatty acids
despite advancements
curve analysis
adolescents infected
68345 ).
15 ).
dc.title.none.fl_str_mv ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <p><b>A.</b> ROC curve view shows an area under ROC curve (AUC) of 0.962. <b>B.</b> Tester analysis shows 93% (70 out of 75) of correct classification. ROC, Receptor operating characteristics.</p>
eu_rights_str_mv openAccess
id Manara_d15feba09d794adfca61478639134ca3
identifier_str_mv 10.1371/journal.pntd.0013691.g004
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/30696720
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.Ricardo E. Correa Fierro (22676430)Noroska Gabriela Mogollón Salazar (22676433)Washington B. Cárdenas (3213597)Evencio Joel Medina-Villamizar (22676436)Jefferson Pastuña-Fasso (22676439)Melanie Ochoa-Ocampo (21222826)Giovanna Morán-Marcillo (22676442)Mary Ernestina Regato Arrata (22676445)Mildred Zambrano (16641645)Joyce Andrade (16641648)Juan Chang (8280894)Saurabh Mehta (367781)Fernanda Bertuccez Cordeiro (16641642)BiochemistryMedicineMicrobiologyCell BiologyBiotechnologyCancerVirologyBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedreceiver operating characteristichighest predictive powerfaster patient screeningcomponent 5 showingcomplex immune responseserum lipid metabolomeevaluate biomarker performancedenv ), triggersserum lipidomics profilingnew diagnostic toolsdiv >< pdisproportionately affects childrenassess group separationpediatric dengue feverdengue virus infectiondenv infected groupdengue virusdengue feverlipid metabolismdiagnostic methodsdiagnosis toolsdengue researchbiomarker potentialbiomarker discoveryz </subtropical regionsmetabolic alterationsmay contributemass spectrometrykey roleglycerol lipidsfatty acidsdespite advancementscurve analysisadolescents infected68345 ).15 ).<p><b>A.</b> ROC curve view shows an area under ROC curve (AUC) of 0.962. <b>B.</b> Tester analysis shows 93% (70 out of 75) of correct classification. ROC, Receptor operating characteristics.</p>2025-11-24T18:25:19ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pntd.0013691.g004https://figshare.com/articles/figure/ROC_curve_analysis_of_a_set_of_biomarkers_obtained_via_untargeted_metabolomics_in_serum_samples_from_children_infected_with_DENV_compared_to_Control_/30696720CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306967202025-11-24T18:25:19Z
spellingShingle ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
Ricardo E. Correa Fierro (22676430)
Biochemistry
Medicine
Microbiology
Cell Biology
Biotechnology
Cancer
Virology
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
receiver operating characteristic
highest predictive power
faster patient screening
component 5 showing
complex immune response
serum lipid metabolome
evaluate biomarker performance
denv ), triggers
serum lipidomics profiling
new diagnostic tools
div >< p
disproportionately affects children
assess group separation
pediatric dengue fever
dengue virus infection
denv infected group
dengue virus
dengue fever
lipid metabolism
diagnostic methods
diagnosis tools
dengue research
biomarker potential
biomarker discovery
z </
subtropical regions
metabolic alterations
may contribute
mass spectrometry
key role
glycerol lipids
fatty acids
despite advancements
curve analysis
adolescents infected
68345 ).
15 ).
status_str publishedVersion
title ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
title_full ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
title_fullStr ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
title_full_unstemmed ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
title_short ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
title_sort ROC curve analysis of a set of biomarkers obtained via untargeted metabolomics in serum samples from children infected with DENV compared to Control.
topic Biochemistry
Medicine
Microbiology
Cell Biology
Biotechnology
Cancer
Virology
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
receiver operating characteristic
highest predictive power
faster patient screening
component 5 showing
complex immune response
serum lipid metabolome
evaluate biomarker performance
denv ), triggers
serum lipidomics profiling
new diagnostic tools
div >< p
disproportionately affects children
assess group separation
pediatric dengue fever
dengue virus infection
denv infected group
dengue virus
dengue fever
lipid metabolism
diagnostic methods
diagnosis tools
dengue research
biomarker potential
biomarker discovery
z </
subtropical regions
metabolic alterations
may contribute
mass spectrometry
key role
glycerol lipids
fatty acids
despite advancements
curve analysis
adolescents infected
68345 ).
15 ).