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Table 1 Discriminating power of individual features

From: Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics

Long CST training set

Short CST training set

Feature

AUC

Feature

AUC

CPS-ratio

0.9128

SpectAlign

0.8466

SpectrAlign

0.9113

CPS-best

0.8322

Ns/Nns-best

0.8538

CPS-ratio

0.8313

CPS-best

0.8532

Aasim-best

0.8259

GC-target

0.8263

Ns/Nns-best

0.8178

GC-probe

0.8231

GC-probe

0.7796

Ns/Nns-ratio

0.7948

GC-target

0.7727

AAsim-best

0.7893

AAID-best

0.7641

Stop-best

0.7854

Ns/Nns-ratio

0.7370

Codon-sim-ratio

0.7674

Aasim-ratio

0.6406

AAID-best

0.7222

AAID-ratio

0.6184

AAsim-ratio

0.6881

Codon-sim-ratio

0.6160

AAID-ratio

0.6626

GFB-ntID

0.6159

GFB-length

0.5587

GFB-length

0.6106

Tv/subs

0.5324

Stop-delta

0.6030

Stop-delta

0.5183

Stop-best

0.5915

GFB-ntID

0.5166

Tv/subs

0.5119

  1. For each feature, Area Under Curve (AUC) calculated from ROC curves calculated with training sets was used as a measure of discriminating power of the feature (see results)
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BMC Bioinformatics

ISSN: 1471-2105

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