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Table 1 Investigated data in contributions from the Data Mining and Machine Learning group at the GAW20

From: Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20

Contribution Sample size Real data Simulated data GWAS EWAS Investigated phenotype(s)
Random forest (Darst) 680   X X X log average post-TG − log average pre-TG
Deep learning (Islam) 993/499a X    X pre-TG, post-TG
Cluster analysis (Kapusta) 446 X   X X relative TG difference, metabolic syndrome
Mixed models (Datta) 680   X X X post TG-pre TG
Gene-set enrichment (Piette) 680   X X X log average post TG/log average pre TG
  1. EWAS epigenome-wide association study, GWAS genome-wide association study, TG triglyceride concentration
  2. aThere were 993 participants in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study, but posttreatment methylation data was only available for 499