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.

翻译：随着工程化生物分子器件的兴起，对定制化生物序列的需求日益增加。通常，特定应用需要制备大量相似的生物序列，这意味着其优化过程需要进行大量（有时甚至因成本过高而不可行）的实验室实验。本文提出了一种基于迁移学习的实验设计工作流，使这一开发过程变得可行。通过将迁移学习代理模型与贝叶斯优化相结合，我们展示了如何通过共享优化任务间的信息来减少实验总数。我们利用基于扩增的诊断检测中竞争DNA分子开发的数据，验证了实验数量的降低效果。采用交叉验证比较了不同迁移学习模型的预测精度，进而对比了这些模型在单目标优化与带惩罚的多目标优化任务中的性能表现。