Additional file 2: of Data integration by multi-tuning parameter elastic net regression

The accuracy in testing dataset, sensitivity and specificity of feature selection from Standard EN and MTP-EN for different simulation settings. MTP-EN achieves better classification and sensitivity in Scenarios 1–3. (PNG 214 kb

Additional file 3: of Data integration by multi-tuning parameter elastic net regression

The optimal penalty ratio parameters versus the change of (A) number of correlated features in the second data type; (B) correlations among features in the second data type; and (C) correlations among features between different platforms. Dots represent the mean of optimal weights and caps represent the standard error of the mean; N = 200 simulation replicates. (PNG 100 kb

Additional file 4: of Data integration by multi-tuning parameter elastic net regression

Listing of features that have more chance to be selected by MTP EN in AML and PRAD datasets. (DOCX 29 kb

Additional file 1: of Data integration by multi-tuning parameter elastic net regression

Self-contained R script for MTP-EN with full example. (R 5 kb