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Table 5 Classification accuracies for different representation strategies for gene-expression data

From: Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

Dataset ϕAcc(F) ϕAcc(X SSGE ) ϕ A c c ( X ̃ P C A S ) ϕ A c c ( X ̃ P C A U S ) ϕ A c c ( X ̃ G E S ) ϕ A c c ( X ̃ G E U S )
Prostate Tumor 73.53 73.53 97.06 100 100 76.47
Breast Cancer Relapse 68.42 63.16 63.16 57.89 63.16 57.89
Lung Cancer 89.93 10.07 99.33 96.64 98.66 100
Lymphoma 58.82 61.76 97.06 76.47 97.06 67.65
  1. Classification accuracies for testing cohorts of 4 different binary class gene-expression datasets, comparing (1) supervised random forest classification of original feature space (F), (2) unsupervised hierarchical clustering of semi-supervised DR space (X SSGE ), and (3) unsupervised hierarchical clustering of consensus embedding space ( X ̃ G E , X ̃ P C A ) .