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Table 2 AUC classification performance for different classification tasks

From: On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

Classification Task

Omic

Feature selection with ReliefF

Feature selection with Limma

AUC

95 % C.I.

BSS

AUC

95 % C.I.

BSS

TAHNADC vs. TumorADC

G

0.99

0.97–1.0

0.89

0.94

0.82–1.0

0.73

M

1.0

1.0–1.0

0.99

0.81

0.58–0.97

0.17

TAHNSCC vs. TumorSCC

M

1.0

0.99–1.0

0.94

0.99

0.96–1.0

0.66

TumorADC vs. TumorSCC

G

0.89

0.83–0.96

0.29

0.90

0.89–0.9

0.81

M

0.97

0.94–0.99

0.71

0.89

0.74–1.0

0.38

TAHNADC vs. TAHNSCC

M

1.0

1.0–1.0

0.92

1.0

1.0–1.0

0.99

TAHN-TumorADC vs. TAHN-TumorSCC

M

0.92

0.89–0.95

0.42

0.94

0.87–1.0

0.56

  1. G: gene expression, M: DNA methylation. The Brier Skill Score is a measurement of calibration of the classifier. A positive value on the BSS means that the classifier is well calibrated. A baseline classification is the work by Chang and Ramoni [22] which obtained an accuracy of 0.95 in the classification task TumorADC vs. TumorSCC