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Table 3 Simulation results using structured sparse mCIA are shown. Sensitivity (Sens), Specificity (Spec), and Matthews correlation coefficient (MCC) for feature selection performance and Angle for estimation performance are calculated. 5-fold cross validation is used to choose the best tuning parameter combination in each method. Values within parenthesis are standard errors

From: Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data

structured sparse multiple CIAmCIA
 a1a2a3a1a2a3
scenarioSensSpecMCCAngleSensSpecMCCAngleSensSpecMCCAngleAngleAngleAngle
10.710.9940.7860.8970.7670.9930.8270.9130.790.9920.8370.9150.8820.8470.830
 (0.284)(0.011)(0.166)(0.088)(0.204)(0.009)(0.106)(0.056)(0.154)(0.008)(0.073)(0.041)(0.025)(0.028)(0.025)
20.790.9790.8140.9180.7870.970.7890.9010.7740.9620.7610.8850.8790.8470.833
 (0.127)(0.021)(0.058)(0.030)(0.089)(0.018)(0.046)(0.024)(0.068)(0.016)(0.041)(0.022)(0.024)(0.027)(0.023)
30.7480.9950.8160.9150.8070.9960.8630.9340.8380.9960.8840.9410.9330.9150.897
 (0.300)(0.010)(0.186)(0.092)(0.221)(0.008)(0.126)(0.064)(0.171)(0.006)(0.091)(0.047)(0.011)(0.011)(0.015)
40.8540.9870.8750.9470.8670.9840.8770.9450.8620.9790.8610.9370.9330.9150.897
 (0.142)(0.016)(0.072)(0.034)(0.097)(0.014)(0.042)(0.021)(0.074)(0.013)(0.038)(0.018)(0.011)(0.011)(0.015)
50.7980.9860.8330.9190.7910.9890.8310.9130.7930.9920.8380.9150.8820.8470.83
 (0.162)(0.016)(0.075)(0.042)(0.162)(0.012)(0.076)(0.043)(0.160)(0.009)(0.073)(0.042)(0.024)(0.028)(0.025)
60.830.9390.7810.9110.8030.9510.7680.8930.7850.9590.760.8840.8830.850.825
 (0.069)(0.029)(0.042)(0.020)(0.069)(0.020)(0.043)(0.021)(0.065)(0.017)(0.043)(0.024)(0.023)(0.025)(0.03)
70.8520.9930.8870.9470.8480.9940.8860.9440.8490.9960.890.9450.9330.9120.9
 (0.158)(0.011)(0.087)(0.044)(0.157)(0.008)(0.083)(0.043)(0.156)(0.006)(0.081)(0.043)(0.011)(0.014)(0.013)
80.8730.9680.8590.9450.8610.9750.8570.9380.860.9810.8640.9370.9330.9130.899
 (0.076)(0.025)(0.039)(0.018)(0.077)(0.017)(0.039)(0.018)(0.072)(0.014)(0.035)(0.016)(0.011)(0.014)(0.013)