. "8629"^^ . . . . . . . . "University of Alberta"@en . . . . . . "Last updated on March 2014"@en . "Data input: Metabolomics produced datasets, uploaded as CSV files containing samples, values and subgroup labeling information. Data output: CSV, PNG, PDF, R history file."@en . . . . . . . . "42630847"^^ . . . "Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC (Receiver Operating Characteristic) curve analyses on metabolomic data sets. ROCCET is designed specifically for performing and assessing a standard binary classification test (disease vs. control). ROCCET accepts metabolite data tables, with or without clinical/observational variables, as input and performs extensive biomarker analysis and biomarker identification using these input data. It operates through a menu-based navigation system that allows users to identify or assess those clinical variables and/or metabolites that contain the maximal diagnostic or class-predictive information. ROCCET supports both manual and semi-automated feature selecti"@en . "Receiver operating characteristic curve testing of metabolomics clustering"@en . . . . . . . "1120707081"^^ . . . . "Receiver Operating Characteristic Curve Explorer and Tester"@en . . . . "Receiver Operating Characteristic Curve Explorer and Tester (ROCCET) is an open-access web server for performing biomarker analysis using ROC (Receiver Operating Characteristic) curve analyses on metabolomic data sets. ROCCET is designed specifically for performing and assessing a standard binary classification test (disease vs. control). ROCCET accepts metabolite data tables, with or without clinical/observational variables, as input and performs extensive biomarker analysis and biomarker identification using these input data. It operates through a menu-based navigation system that allows users to identify or assess those clinical variables and/or metabolites that contain the maximal diagnostic or class-predictive information. ROCCET supports both manual and semi-automated feature selection and is able to automatically generate a variety of mathematical models that maximize the sensitivity and specificity of the biomarker(s) while minimizing the number of biomarkers used in the biomarker model. ROCCET also supports the rigorous assessment of the quality and robustness of newly discovered biomarkers using permutation testing, hold-out testing and cross-validation."@en . . . . . . . . . . "Receiver operating characteristic curve testing of metabolomics clustering" .