Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and enhancing model robustness in situations in which data are limited. This review critically assesses recent advances in the application of TL in bioprocess engineering. From genomic analysis to bioreactor modeling and analytics, TL can increase the accuracy of models aiming to predict protein functions, growth, and product formation as well as retention times in chromatographic processes. Despite its potential, several challenges remain, including data heterogeneity and model transferability. Future research will most likely focus on integrating TL with hybrid and physics-informed modeling frameworks, developing standardized benchmark data sets, and exploiting TL to extract relevant information from publicly available data sets. Overall, TL provides a way forward for creating more data-efficient, generalizable, and interpretable models for bioprocess engineering.
Díaz et al. (Mon,) studied this question.