ROC (receiver operating characteristic) curve: Principles and application in biology

Laboratoire de biochimie, toxicologie cliniques, Hôpital d'Instruction des Armées du Val-de-Grâce, Paris, France.
Annales de biologie clinique (Impact Factor: 0.28). 03/2005; 63(2):145-54.
Source: PubMed


Laboratory test's diagnostic performances are generally estimated by means of their sensibility, specificity and positive and negative predictive values. Unfortunately, these indices reflect only imperfectly the capacity of a test to correctly classify subjects into clinically relevant subgroups. The appeal to ROC (receiver operating characteristic) curve appears as a tool of choice for this evaluation. Used in the medical domain since the 60s, ROC curve is a graphic representation of the relation existing between the sensibility and the specificity of a test, calculated for all possible cut-off. It allows the determination and the comparison of the diagnostic performances of several tests. It is also used to consider the optimal cut-off of a test, by taking into account epidemiological and medical - economic data of the disease. Used in numerous medical domains, this statistical tool is easily accessible thanks to the development of computer softwares. This article exposes the principles of construction and exploitation of a ROC curve.

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    • "To facilitate the use of the model in clinical practice, the regression coefficients associated with the identified predictors in the final model were transformed by multiplication with a factor 4, rounded off to the nearest integer, into scores to obtain an aggregate score by adding up the scores. The discriminative power of the model was assessed using the area under the receiver operating characteristic (ROC) curve (AUC) [20]. To develop an algorithm for clinical approach to the prevention of falls among elderly living in the community, two cutoffs were discussed: the cutoff maximizing the Youden index and the cutoff maximizing the sum of the positive predictive value (PPV) and negative predictive value (NPV). "
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    ABSTRACT: To develop a simple clinical screening tool for community-dwelling older adults. A prospective multicenter cohort study was performed among healthy subjects of 65 years and older, examined in 10 health examination centers for the French health insurance. Falls were ascertained monthly by telephone for 12-month follow-up. Multivariate analyses using Cox regression models were performed. Regression coefficients of the predictors in the final model were added up to obtain the total score. The discriminative power was assessed using the area under the curve (AUC). Thousand seven hundred fifty-nine subjects were included. The mean age was 70.7 years and 51% were women. At least one fall occurred among 563 (32%) participants. Gender, living alone, psychoactive drug use, osteoarthritis, previous falls, and a change in the position of the arms during the one-leg balance (OLB) test were the strongest predictors. These predictors were used to build a risk score. The AUC of the score was 0.70. For a cutoff point of 1.68 in a total of 4.90, the positive predictive value and negative predictive value were 72.0% and 72.7%, respectively. A screening tool with five risk factors and the OLB test could predict falls in healthy community-dwelling older adults.
    Journal of clinical epidemiology 04/2011; 64(10):1152-60. DOI:10.1016/j.jclinepi.2010.12.014 · 3.42 Impact Factor
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    • "The peak of the resulting profile is considered to correspond to the location of the B-cell epitope for the protein concerned . Unfortunately, to one's disappointment, a recent study by Blythe and Flower (2005) led to the conclusion that ''single-scale amino acid propensity profiles cannot be used to predict epitope location reliably'', as reflected by the fact that even the best amino acid propensity scales could only yield a success rate marginally better than that by randomly using ROC (receiver operating characteristics) plot (Delacour et al., 2005). As the data of linear B-cell epitopes are accumulating, it is possible to increase the prediction quality by using machine learning approaches (Sollner, 2006; Sollner and Mayer, 2006), and the prediction accuracy has been improved by so doing. "
    J Chen · H Liu · J Yang · K-C Chou ·
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    ABSTRACT: Identification of antigenic sites on proteins is of vital importance for developing synthetic peptide vaccines, immunodiagnostic tests and antibody production. Currently, most of the prediction algorithms rely on amino acid propensity scales using a sliding window approach. These methods are oversimplified and yield poor predicted results in practice. In this paper, a novel scale, called the amino acid pair (AAP) antigenicity scale, is proposed that is based on the finding that B-cell epitopes favor particular AAPs. It is demonstrated that, using SVM (support vector machine) classifier, the AAP antigenicity scale approach has much better performance than the existing scales based on the single amino acid propensity. The AAP antigenicity scale can reflect some special sequence-coupled feature in the B-cell epitopes, which is the essence why the new approach is superior to the existing ones. It is anticipated that with the continuous increase of the known epitope data, the power of the AAP antigenicity scale approach will be further enhanced.
    Amino Acids 10/2007; 33(3):423-8. DOI:10.1007/s00726-006-0485-9 · 3.29 Impact Factor
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    ABSTRACT: The ability to predict antigenic sites on proteins is crucial for the production of synthetic peptide vaccines and synthetic peptide probes of antibody structure. Large number of amino acid propensity scales based on various properties of the antigenic sites like hydrophilicity, flexibility/mobility, turns and bends have been proposed and tested previously. However these methods are not very accurate in predicting epitopes and non-epitope regions. We propose algorithms that combine 14 best performing individual propensity scales and give better prediction accuracy as compared to individual scales.
    Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE; 09/2005
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