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Fig. 7 | BMC Bioinformatics

Fig. 7

From: Hydropathicity-based prediction of pain-causing NaV1.7 variants

Fig. 7

Binary classification of missense SCN9A-gene mutation sites based on a weighted distance average. a ROC curve constructed from data of distances between mutation sites and the weighted combination of SF’s critical-point and HP’s boundary (for construction of data set see Additional file 1: S11). Optimal threshold value \(\sim\)9.6 Å corresponds to specificity and sensitivity values of 0.805 and 0.937, respectively. Area under ROC curve is 0.872. b Visualization of ROC curve data. Optimal threshold value \(\sim\)9.6 Å is marked with black dashed line. ROC curve is constructed in R [73] by using the pROC package [93]. Two sets of missense SCN9A-gene mutation sites are employed; a pain-related set containing IEM, PPD and SFN mutation sites, and a neutral set containing mutation sites which are not expected to associate with pain disease phenotypes (Additional file 1: S8)

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