In Bioresource technology
Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs. unsupervised) per application, as well as on their system boundaries (engineered vs. natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.
Ji Lim Seung, Son Moon, Jin Ki Seo, Suh Sang-Ik, Chung Jaeshik
2022-Dec-21
Bioprocess, Deep learning, Engineered system, Machine learning, Natural system
