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Table 1 Blind accuracies for the SVM models using different filtering methods

From: Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method

Filtering method

Number of genes

Accuracy (%) SVM model

PART

1000

88.9

NSC

1000

66.7

S2N

1000

77.8

SAM

1000

77.8

Student's t-test

1000

66.7

U-test

1000

66.7

Welch's t-test

1000

66.7

Random selection1

1000

55.6

No filtering

12241

55.6

  1. 1 The SVM model was constructed by using 1000 probes selected randomly. This process was repeated 1000 times. Average accuracies of 1000 SVM models were calculated.
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BMC Bioinformatics

ISSN: 1471-2105

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