Abstract：
Modern machine learning has the potential to fundamentally change the way bioprocesses are developed. In particular, horizontal knowledge transfer methods, which seek to exploit data from historical processes to facilitate process development for a new product, provide an opportunity to rethink current workflows. In this work, we first assess the potential of two knowledge transfer approaches, meta learning and one-hot encoding, in combination with Gaussian process (GP) models. We compare their performance with GPs trained only on data of the new process, that is, local models. Using simulated mammalian cell culture data, we observe that both knowledge transfer approaches exhibit test set errors that are approximately halved compared to those of the local models when two, four, or eight experiments of the new product are used for training. Subsequently, we address the question whether experiments for a new product could be designed more effectively by exploiting existing knowledge. In particular, we suggest to specifically design a few runs for the novel product to calibrate knowledge transfer models, a task that we coin calibration design. We propose a customized objective function to identify a set of calibration design runs, which exploits differences in the process evolution of historical products. In two simulated case studies, we observed that training with calibration designs yields similar test set errors compared to common design of experiments approaches. However, the former requires approximately four times fewer experiments. Overall, the results suggest that process development could be significantly streamlined when systematically carrying knowledge from one product to the next.
摘要：
现代机器学习有可能从根本上改变生物过程的发展方式。特别是,横向知识转移方法，寻求利用历史过程中的数据来促进新产品的过程开发，提供重新思考当前工作流程的机会。在这项工作中,我们首先评估两种知识转移方法的潜力，元学习和独热编码，结合高斯过程(GP)模型。我们将他们的表现与仅在新流程数据上训练的GP进行比较，也就是说,本地模型。使用模拟的哺乳动物细胞培养数据，我们观察到，两种知识转移方法都表现出测试集误差，与局部模型相比，当两个模型时，四,或新产品的八个实验用于培训。随后，我们解决的问题是否可以通过利用现有知识更有效地设计新产品的实验。特别是,我们建议专门为新产品设计一些运行来校准知识转移模型，我们硬币校准设计的任务。我们提出了一个定制的目标函数来识别一组校准设计运行，利用历史产品演变过程中的差异。在两个模拟案例研究中，我们观察到,与普通实验设计相比,使用校准设计进行训练会产生相似的测试集误差.然而,前者需要大约少四倍的实验。总的来说,结果表明，当系统地将知识从一种产品传递到另一种产品时，工艺开发可以显着简化。
