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Multiclass ROC curves for the eight machine learning models generated using combined metabolite and behavioral markers as input data. The respective AUC values for each model are also shown, illustrating their effectiveness in class discrimination.

Journal: Nmr in Biomedicine

Article Title: Urinary Metabolic Biomarkers of Attentional Control in Children With Attention‐Deficit/Hyperactivity Disorder: A Dimensional Approach Through 1 H NMR‐Based Metabolomics

doi: 10.1002/nbm.70088

Figure Lengend Snippet: Multiclass ROC curves for the eight machine learning models generated using combined metabolite and behavioral markers as input data. The respective AUC values for each model are also shown, illustrating their effectiveness in class discrimination.

Article Snippet: The optimized acquisition parameters are those already reported in Tristán et al. [ ] For structural elucidation of the metabolite set present in children's urine, two‐dimensional experiments including 1 H– 1 H COSY, 1 H– 1 H TOCSY, 1 H– 13 C HSQC, and 1 H– 13 C HMBC of a representative sample were recorded using standard Bruker sequences as described in Tristán et al. [ ] Additionally, this identification of the metabolic profile was also supported by the assistance of public databases—The Human Metabolome Database (HMDB, http://www.hmdb.ca ) and Complex Mixture Analysis Database (COLMAR, http://spin.ccic.ohio‐state.edu/index.php/colmar )—and private ones—Chenomx, Metabolite Reference Database (Bruker, bbiorefcode)—and literature searches.

Techniques: Generated