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Table 3 Datasets Summary

From: A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data

Data set

Number of the case and control samples

Number of the features (after selection)

Reference

 

GSE14520

225, 220

135

Roessler S, Jia HL, Budhu A, Forgues M et al. A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients. Cancer Res 2010 Dec 15;70 [24]:10202-12. PMID: 21,159,642

 

GSE25097

268, 243

135

Tung EK, Mak CK, Fatima S, Lo RC et al. Clinicopathological and prognostic significance of serum and tissue Dickkopf-1 levels in human hepatocellular carcinoma. Liver Int 2011 Nov;31 [10]:1494 − 504. PMID: 21,955,977

 

GSE36376

240, 193

135

Lim HY, Sohn I, Deng S, Lee J et al. Prediction of disease-free survival in hepatocellular carcinoma by gene expression profiling. Ann Surg Oncol 2013 Nov;20 [12]:3747-53. PMID: 23,800,896

 

Cambridge Baby Growth Study (CBGS) cohorts

CBGS-1 85 HM, 87 FM; CBGS-2 43 HM, 25 FM, 27 HM + FM; POPS 16 HM, 11 FM

218

Acharjee, A., Prentice, P., Acerini, C. et al. The translation of lipid profiles to nutritional biomarkers in the study of infant metabolism. Metabolomics 13, 25 (2017). https://doi.org/10.1007/s11306-017-1166-2

 
  1. Summary of public datasets used in this study. Reference refers to the earliest citation present on the corresponding NCBI Gene Expression Omnibus [28] page. Number of features reflects the input data to the corresponding GAN model