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Fig. 2 | BioData Mining

Fig. 2

From: Joint analysis of multiple high-dimensional data types using sparse matrix approximations of rank-1 with applications to ovarian and liver cancer

Fig. 2

Distribution of \( \Delta \mathrm{AUROC} \) values comparing JAMMIT detection performance with two other algorithms in simulated data. Panels a and b show the distributions of \( \Delta \mathrm{AUROC} \) values equal to the AUROC for JAMMIT minus the AUROC for JIVE for the detection of two simulated signals, SSig1 and SSig2,  in 1000 simulated MMDS as described in the Methods section of this paper. Similarly, panels b and c show \( \Delta \mathrm{AUROC} \) distributions for JAMMIT versus PLS to detect SSig1 and SSig2 in the same set of simulated MMDS used to evaluate JAMMIT versus JIVE. Each \( \Delta \mathrm{AUROC} \) distribution was based on a normal kernel smoothing function evaluated at 100 equally spaces points using MATLAB’s ksdensity function. Note for each distribution, the area under the distribution curve is equal to one and most of this area (i.e., probability measure) is concentrated on the positive x-axis to the right of the vertical green line positioned at \( x=0 \). This result indicates that on average JAMMIT outperformed both JIVE and PLS in detecting the two simulated signals over a wide range of SNR scenarios

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