  Predictive modeling from high-dimensional genomic data is often preceded by a
dimension reduction step, such as principal components analysis (PCA). However,
the application of PCA is not straightforward for multi-source data, wherein
multiple sources of 'omics data measure different but related biological
components. In this article we utilize recent advances in the dimension
reduction of multi-source data for predictive modeling. In particular, we apply
exploratory results from Joint and Individual Variation Explained (JIVE), an
extension of PCA for multi-source data, for prediction of differing response
types. We conduct illustrative simulations to illustrate the practical
advantages and interpretability of our approach. As an application example we
consider predicting survival for Glioblastoma Multiforme (GBM) patients from
three data sources measuring mRNA expression, miRNA expression, and DNA
methylation. We also introduce a method to estimate JIVE scores for new samples
that were not used in the initial dimension reduction, and study its
theoretical properties; this method is implemented in the R package R.JIVE on
CRAN, in the function 'jive.predict'.
