  The features in high dimensional biomedical prediction problems are often
well described with lower dimensional manifolds. An example is genes that are
organised in smaller functional networks. The outcome can then be described
with the factor regression model. A benefit of the factor model is that is
allows for straightforward inclusion of unlabeled observations in the
estimation of the model, i.e., semi-supervised learning. In addition, the high
dimensional features in biomedical prediction problems are often well
characterised. Examples are genes, for which annotation is available, and
metabolites with $p$-values from a previous study available. In this paper, the
extra information on the features is included in the prior model for the
features. The extra information is weighted and included in the estimation
through empirical Bayes, with Variational approximations to speed up the
computation. The method is demonstrated in simulations and two applications.
One application considers influenza vaccine efficacy prediction based on
microarray data. The second application predictions oral cancer metastatsis
from RNAseq data.
