Transfer Learning Bayesian Optimization to Design Competitor DNA
  Molecules for Use in Diagnostic Assays
Ruby Sedgwick John P. Goertz Molly M. Stevens Ruth Misener Mark van der Wilk
With the rise in engineered biomolecular devices, there is an increased need
for tailor-made biological sequences. Often, many similar biological sequences
need to be made for a specific application meaning numerous, sometimes
prohibitively expensive, lab experiments are necessary for their optimization.
This paper presents a transfer learning design of experiments workflow to make
this development feasible. By combining a transfer learning surrogate model
with Bayesian optimization, we show how the total number of experiments can be
reduced by sharing information between optimization tasks. We demonstrate the
reduction in the number of experiments using data from the development of DNA
competitors for use in an amplification-based diagnostic assay. We use
cross-validation to compare the predictive accuracy of different transfer
learning models, and then compare the performance of the models for both single
objective and penalized optimization tasks.