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| Receiver Operating Characteristic Curve Explorer and Tester ： ウィキペディア英語版 |
Receiver Operating Characteristic Curve Explorer and Tester
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.
==Background – ROC curves in biomarker discovery==
Biomarkers are commonly defined as measured characteristics that may be used as indicators of some biological state or condition. They may be genes, chemicals, proteins, physiological parameters, imaging data or histological measurements. Biomarkers can consist of single components (i.e. blood glucose) or multiplc components (a biomarker panel such as acylcarnitines). Medical biomarkers fall into 5 major categories: 1) diagnostic (used to identify if you have a disease or condition); 2) prognostic (used to determine how well you will do with the disease or condition); 3) predictive (used to determine if you may get the disease); 4) efficacy or monitoring (used to determine how well a drug or treatment is doing in fighting the disease) and 5) exposure (used to determine if you have been exposed to a drug, food, toxin or other kind of substance). Good biomarkers should exhibit good sensitivity (the fraction of correctly identified true positives) and good specificity (the fraction of correctly identified true negatives). A perfect biomarker or biomarker panel would be 100% sensitive (predict all people in the sick group as being sick) and 100% specific (not predicting anyone from the healthy group as being sick). However, since few things in life are perfect, there is often a trade-off between sensitivity and specificity. In medical biomarker studies it is becoming increasingly common to report this tradeoff in sensitivity and specificity using a Receiver Operating Characteristic (ROC) curve. ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on the x axis. An image of different ROC curves is shown in Figure 1.
ROC curves provide a simple visual method for one to determine the boundary limit (or the separation threshold) of a biomarker or a combination of biomarkers for the optimal combination of sensitivity and specificity. The AUC (area under the curve) of the ROC curve reflects the overall accuracy and the separation performance of the biomarker (or biomarkers), and can be readily used to compare different biomarker combinations or models. As a rule of thumb, the fewer the biomarkers that one uses to maximize the AUC of the ROC curve, the better.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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