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Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction. | LitMetric

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction.

Bioresour Technol

Water Cycle Research Center, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea; Division of Energy and Environmental Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea. Electronic address:

Published: February 2023

AI Article Synopsis

  • Recent advancements in machine learning (ML) have significantly impacted various research and industry fields by solving complex problems that traditional methods struggle with.
  • The application of ML in bioprocesses faces challenges due to low interpretability and incompatibility, largely because these processes involve the intricate interactions of living cells.
  • While there have been some hybrid approaches that show promise in improving accuracy, further efforts are needed to enhance the usability and clarity of ML models for better application in bioprocess contexts.

Article Abstract

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.

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Source
http://dx.doi.org/10.1016/j.biortech.2022.128518DOI Listing

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