Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers.
<p>The horizontal coordinates indicate the false positive rate; the vertical coordinates indicate the true positive rate; and the area under the curve (AUC) value indicates the prediction accuracy. ROC, Receptor operating characteristics.</p>
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2025
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| _version_ | 1849927643274674176 |
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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 | 2025-11-24T18:25:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pntd.0013691.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Univariate_ROC_curves_for_individual_analysis_of_the_15_i_m_z_i_suggested_as_biomarkers_/30696717 |
| 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 | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The horizontal coordinates indicate the false positive rate; the vertical coordinates indicate the true positive rate; and the area under the curve (AUC) value indicates the prediction accuracy. ROC, Receptor operating characteristics.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_feef8088b78d1c84a1d32d794003ffb3 |
| identifier_str_mv | 10.1371/journal.pntd.0013691.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30696717 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers.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>The horizontal coordinates indicate the false positive rate; the vertical coordinates indicate the true positive rate; and the area under the curve (AUC) value indicates the prediction accuracy. ROC, Receptor operating characteristics.</p>2025-11-24T18:25:18ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pntd.0013691.g003https://figshare.com/articles/figure/Univariate_ROC_curves_for_individual_analysis_of_the_15_i_m_z_i_suggested_as_biomarkers_/30696717CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306967172025-11-24T18:25:18Z |
| spellingShingle | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. 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 | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| title_full | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| title_fullStr | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| title_full_unstemmed | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| title_short | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| title_sort | Univariate ROC curves for individual analysis of the 15 <i>m/z</i> suggested as biomarkers. |
| 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 ). |