Performance of Cox-TGDR-specific and Cox-filter on NSCLC data.

<p>¹ a higher ICC cut-off (90%) was used.</p><p>Performance of Cox-TGDR-specific and Cox-filter on NSCLC data.</p

Venn diagrams of 33- and 13-gene signatures.

<p>A) On the individual gene level. B) On the enriched pathway level. 33-gene and 13-gene signatures were obtained using Cox-TGDR-specific algorithm with one being trained on the microarray data and the other on the RNA-seq data. Here,↓ and ↑ indicate a negative and positive association with hazard of death, respectively.</p

The estimated coefficients of the genes selected by multi-TGDR in the psoriasis data.

<p>Normal skin tissues from controls served as the reference. NL: Non-Lesional skin; LS: Lesional skin.</p

Performance of classifiers for Lung Cancer data.

<p>Performance of classifiers for Lung Cancer data.</p

The results for simulated data.

<p>The results for simulated data.</p

Psoriasis LS versus NL genes by TGDR and Meta-TGDR after Bagging.

<p>Non-lesional skin samples serve as the reference. Bagging frequency >30% for TGDR and >40% for Meta-TGDR.</p

Psoriasis 3 classes genes selected by multi-TGDR after Bagging.

<p>There are 39 genes in the final model. Normal tissue from healthy controls serves as the reference. Bagging frequency >40%.</p

Multi-TGDR genes for lung cancer data after Bagging.

<p>Here, AC-I serves as the reference. Bagging frequency >40%.</p

Performance of Classifiers for Psoriasis data.

<p>A. Comparison between TGDR and Meta-TGDR for binary classifiers. B. Comparisons between TGDR and Meta-TGDR for 3-class classifiers.</p

Additional file 1: of Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

R codes for the weighted-SAMGSR algorithm. (DOCX 76 kb