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. Author manuscript; available in PMC: 2021 Jan 1.
Published in final edited form as: J Invest Dermatol. 2019 Sep 30;140(1):164–173.e7. doi: 10.1016/j.jid.2019.06.126

Figure 2: MicroRNA ratio-trained model classifies melanocytic lesions.

Figure 2:

a) MiRNAs that classify micro-dissected sections identified by Boruta feature selection (FS) compared to shadow max (X) and mean (M) features. b) Normalized miRNA-Seq counts from micro-dissected regions. Boxes indicate mean, first and third quartiles. c) Normalized expression of FS-miRNAs in published studies. AUC of classification using each miRNA in parenthesis. d) Model of the expected observed expression of MIR21–5p with variation in tumor content compared to expected observed expression from pure nevus samples (purple box). e) Heatplot of p values when comparing expected observed MIR21–5p expression (left) and expected observed MIR21–5p:MIR2n-5p ratio (right) of melanoma samples with variation in tumor content to nevus. f) ME-miRNA:MD-miRNA ratios from published studies. AUC of classification using each ratio in parenthesis. g-h) ROC curves for cross-validation of miRNA expression (g) and ratio (h) classification models trained on aggregate published studies. Naïve Bayes (NB), Random Forest (RF), Logistic Regression (GLM).