  We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for
data integration, based on combining multiple matrix factorisation methods,
that can be used for in- and out-of-matrix prediction of missing values. The
model is very general and can be used to integrate many datasets across
different entity types, including repeated experiments, similarity matrices,
and very sparse datasets. We apply our method on two biological applications,
and extensively compare it to state-of-the-art machine learning and matrix
factorisation models. For in-matrix predictions on drug sensitivity datasets we
obtain consistently better performances than existing methods. This is
especially the case when we increase the sparsity of the datasets. Furthermore,
we perform out-of-matrix predictions on methylation and gene expression
datasets, and obtain the best results on two of the three datasets, especially
when the predictivity of datasets is high.
