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Big data and machine learning driven bioprocessing - Recent trends and critical analysis.
Yang, Chao-Tung; Kristiani, Endah; Leong, Yoong Kit; Chang, Jo-Shu.
  • Yang CT; Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan.
  • Kristiani E; Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia.
  • Leong YK; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan.
  • Chang JS; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407224, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan 701, Taiwan. Electronic addre
Bioresour Technol ; 372: 128625, 2023 Mar.
Article in English | MEDLINE | ID: covidwho-2287473
ABSTRACT
Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Big Data Type of study: Prognostic study Language: English Journal: Bioresour Technol Journal subject: Biomedical Engineering Year: 2023 Document Type: Article Affiliation country: J.biortech.2023.128625

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Artificial Intelligence / Big Data Type of study: Prognostic study Language: English Journal: Bioresour Technol Journal subject: Biomedical Engineering Year: 2023 Document Type: Article Affiliation country: J.biortech.2023.128625