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Table 4 Classification results in terms of AUC obtained on the E-MTAB-1803, E-TABM-147 and GSE32894 datasets with Random Forest (RF) and Support Vector Machine (SVM) classifiers using input signals LATNET P, LATNET A, PCA, SVD and NMF

From: Latent network-based representations for large-scale gene expression data analysis

 

LatNet A

LatNet P

PCA

SVD

NMF

 

RF

SVM

RF

SVM

RF

SVM

RF

SVM

RF

SVM

E-MTAB-1803

0.93

0.91

0.94

0.85

0.93

0.87

0.93

0.90

0.92

0.92

E-TABM-147

0.88

0.87

0.91

0.91

0.87

0.87

0.86

0.90

0.84

0.89

GSE32894

0.83

0.82

0.83

0.83

0.75

0.75

0.77

0.81

0.82

0.82

  1. In bold are the best AUC achieved on each dataset