Fig. 1

Scheme for kernel aggregation and prediction models. (1) Design matrices for CpG sites, mRNAs, and miRNAs are aggregated to form a linear or Gaussian kernel matrix that measures the similarity of samples. (2) Clinical variables are regressed out of the outcomes IQ and SRS and from the omic kernels to limit influence from these variables. (3) Using 50-fold Monte Carlo cross-validation on 75–25% training-test splits, we train prediction models with the kernel matrices for IQ and SRS in the training set and predict in the test sets. Prediction is assessed in every fold with adjusted \({R}^{2}\) and averaged for an overall prediction metric