Fig. 2: MuTATE is an explainable ML algorithm for accurate multi-target structural modeling. | npj Health Systems

Fig. 2: MuTATE is an explainable ML algorithm for accurate multi-target structural modeling.

From: MuTATE: an interpretable multi-endpoint machine learning framework for automated molecular subtyping in cancer

Fig. 2

Multivariable analyses assessing method and model performance in 18,400 synthetic multi-target dataset simulations (adjusted for simulated sample size, number of targets, number of features, inter-target correlation, model depth, and run ID). Boxplots represent regression coefficient distributions across 18,400 simulations. Points indicate mean coefficient estimates with 95% CI. Positive coefficients reflect improved performance relative to CART (e.g., higher TDR), while negative coefficients reflect lower error or FDR. Whiskers represent interquartile range, and overlaid points display mean coefficients with 95% confidence intervals, derived from multivariable regression adjusting for simulation conditions.

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