  To date, a large number of experiments are performed to develop a biochemical
process. The generated data is used only once, to take decisions for
development. Could we exploit data of already developed processes to make
predictions for a novel process, we could significantly reduce the number of
experiments needed. Processes for different products exhibit differences in
behaviour, typically only a subset behave similar. Therefore, effective
learning on multiple product spanning process data requires a sensible
representation of the product identity. We propose to represent the product
identity (a categorical feature) by embedding vectors that serve as input to a
Gaussian Process regression model. We demonstrate how the embedding vectors can
be learned from process data and show that they capture an interpretable notion
of product similarity. The improvement in performance is compared to
traditional one-hot encoding on a simulated cross product learning task. All in
all, the proposed method could render possible significant reductions in
wet-lab experiments.
