Selected article for: "AUC ROC curve and positive rate"
Author: Yujia Xiang; Quan Zou; Lilin Zhao
Title: VPTMdb: a viral post-translational modification database
  • Document date: 2020_4_2
    • ID: kl99afiu_47
    Snippet: Sensitivity (Sn), Specificity (Sp), F1-score, and Mathews Correlation Coefficient (MCC) were applied to estimate the prediction performance (Supplementary Materials Si2) . In addition, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the overall performance of the model. The ROC curve is a continuous line plotted by the false positive rate (FPR) as the Xcoordinate and true positive rat.....
    Document: Sensitivity (Sn), Specificity (Sp), F1-score, and Mathews Correlation Coefficient (MCC) were applied to estimate the prediction performance (Supplementary Materials Si2) . In addition, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were used to evaluate the overall performance of the model. The ROC curve is a continuous line plotted by the false positive rate (FPR) as the Xcoordinate and true positive rate (TPR) as the Y-coordinate. The higher the AUC value, the better the performance of the classifier.

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