Figure 2.

M3-LS model accurately predicts M3 subtype in AML

(A) Random forest, XGBoost, and M3-LS model were used to predict M3 samples, and Receiver operating characteristic curve (ROC) analysis was used to evaluate the prediction model.

(B) The proportion of M3-like samples predicted by the optimized model in each subtype, amaranth represents the proportion of samples predicted to be M3-like subtype, and yellow represents the proportion of samples not predicted to be M3-like subtype.

(C) Model scores were compared for each AML subtype. Boxes and violin plots showing median, 25th and 75th percentiles. Purple box and violin plots represent model scores for all AML samples except M3 subtype. Wilcoxon Rank-Sum test was used for statistical calculation. Validation cohort-1 and 2, (D and G) Random forest, XGBoost machine learning models and M3-like scoring index were used to predict M3 samples.

(E and H) The proportion of M3-like samples predicted by the optimized model in each subtype.

(F and I) Model scores were compared for each AML subtype.