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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| _version_ | 1849927643272577024 |
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| 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 ). |