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大数据和机器学习驱动的生物工艺学——最新趋势和关键分析。

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

机构信息

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

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

出版信息

Bioresour Technol. 2023 Mar;372:128625. doi: 10.1016/j.biortech.2023.128625. Epub 2023 Jan 13.

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.

摘要

鉴于机器学习算法在彻底改变生物工程领域的潜力,本文对生物处理领域中人工智能(AI)相关文献进行了研究和总结。采用自然语言处理(NLP)来探索研究领域的方向。从 Scopus 中提取了 2013 年至 2022 年具有特定 AI 生物处理关键词的所有论文,并将其分为 2013 年至 2017 年和 2018 年至 2022 年两个五年期,比较了过去和现在的研究方向。在此基础上,对最近五年的部分样本论文进行了进一步的回顾和分析。结果表明,过去五年中有 50%的出版物集中在混合模型、人工神经网络、生物制药制造和生物炼制等相关主题上。对结果的总结和分析表明,在生物处理领域实施 AI 可以改进设计和工艺工程策略。

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