Transfer Learning Bayesian Optimization to Design Competitor DNA Molecules for Use in Diagnostic Assays

arXiv:2402.17704v1 Announce Type: cross
Abstract: 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 …

abstract application arxiv bayesian cs.lg design devices diagnostic dna lab meaning molecules optimization q-bio.qm stat.ml transfer transfer learning type