  Translating potential disease biomarkers between multi-species 'omics'
experiments is a new direction in biomedical research. The existing methods are
limited to simple experimental setups such as basic healthy-diseased
comparisons. Most of these methods also require an a priori matching of the
variables (e.g., genes or metabolites) between the species. However, many
experiments have a complicated multi-way experimental design often involving
irregularly-sampled time-series measurements, and for instance metabolites do
not always have known matchings between organisms. We introduce a Bayesian
modelling framework for translating between multiple species the results from
'omics' experiments having a complex multi-way, time-series experimental
design. The underlying assumption is that the unknown matching can be inferred
from the response of the variables to multiple covariates including time.
