Big data and machine learning driven bioprocessing - Recent trends and critical analysis.

Artificial Intelligence Big Data Machine Learning Algorithms Natural Language Processing
Artificial intelligence Artificial neural networks Bioprocessing Hybrid models Machine learning Natural language processing

Journal

Bioresource technology
ISSN: 1873-2976
Titre abrégé: Bioresour Technol
Pays: England
ID NLM: 9889523

Informations de publication

Date de publication:
Mar 2023
Historique:
received: 22 11 2022
revised: 09 01 2023
accepted: 11 01 2023
pubmed: 16 1 2023
medline: 16 2 2023
entrez: 15 1 2023
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1016/j.biortech.2023.128625 PMID: 36642201
pubmed: 36642201
pii: S0960-8524(23)00051-2
doi: 10.1016/j.biortech.2023.128625
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

128625

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Chao-Tung Yang (CT)

Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407224, Taiwan.

Endah Kristiani (E)

Department of Computer Science, Tunghai University, Taichung 407224, Taiwan; Department of Informatics, Krida Wacana Christian University, Jakarta 11470, Indonesia.

Yoong Kit 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.

Jo-Shu 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 address: changjs@thu.edu.tw.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Big data and machine learning driven...
questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Systèmes d'information › Mégadonnées › Big data and machine learning driven...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Big data and machine learning driven...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Big data and machine learning driven...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Traitement du langage naturel › Big data and machine learning driven...