I am reading this paper, and Figure 4 looks as follows:

enter image description here

As I understand ROC curves the numbers on the curve are typically the thresholds for binary classifications. Hence how comes that here we have the same thresholds (for instance ) repeatedly?


Legend Fig. 4:

Fig. 4. Variant prediction of LOF and GOF effects in Navs and Cavs. We trained our statistical model on 746 variants in 12 genes whose functional effects were inferred from disease phenotypes. Here, we show how the model predicts LOF/GOF variant effects in two datasets: 82 disease phenotypes, randomly picked from training data before model training (A and C), and 87 separate functionally tested variants (B and D). (A) Prediction of LOF disease phenotypes, sensitivity = 0.76, specificity = 0.83. (B) Prediction of LOF electrophysiology experiments, sensitivity = 0.74,specificity = 0.72. (C) Prediction of GOF disease phenotypes, sensitivity = 0.83, specificity = 0.76. (D) Prediction of GOF electrophysiology experiments, sensitivity = 0.72, specificity = 0.74. The area under the ROC curve was 0.85 for phenotype-based LOF/GOF prediction and 0.73 for electrophysiology-based LOF/GOF prediction. (E) Feature im- portance for prediction of GOF versus LOF. The relative influence of features on the prediction normalized to sum to 100 is computed as described in (89). Of 89 features that went into the prediction, only the 18 features that have a relative influence >0.05 on the prediction are shown.


1 Answer 1


That's most likely just a rounding issue. The thresholds are likely not exactly 0.2, 0.3 etc, but the figure doesn't show enough significant digits to tell them apart.

  • $\begingroup$ Thank you!...... $\endgroup$
    – Pugl
    Commented Jun 3, 2021 at 11:15

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