Skip to main content

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
Back to article page

Genome Medicine

ISSN: 1756-994X

Contact us