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Table 3 Overall accuracy of the SVM classifier trained using the genes proposed by Martinez et al. and the genes selected via SVM-RFE and stability selection in this study

From: Predicting cancer type from tumour DNA signatures

Classification task

25-gene panel in Martinez et al.

Top 25 SVM-RFE-based SPM genes

Top 25 SVM-RFE-based SPM and CNA genes

28 cancer types of this study

30.4 %

39.0 %

67.7 %

10 cancer types of Martinez et al.

54.6 %

57.4 %

85.4 %

  1. The classifier was tested on 1661 unseen tumour samples
  2. CNA copy number altered, SPM somatic point-mutated, SVM support vector machine, SVM-RFE SVM recursive feature eliminatio