提高胜算:人工智能与盘数大反常

IF 5.7 2区 生物学 Microbial Biotechnology Pub Date : 2024-08-30 DOI:10.1111/1751-7915.70004
Detmer Sipkema
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引用次数: 0

摘要

下一代 DNA 测序表明,大平板计数异常,即存在于环境中的细菌与可从该环境中培养获得的细菌之间的差异,比最初想象的更大,更持久。这阻碍了对细菌生理学的基本了解,也阻碍了未培养的大多数细菌在生物技术上的应用。有了序列大数据作为基础,人工智能(AI)可能会改变细菌分离工作的游戏规则,并为最有前景的培养基和条件提供方向,从而用于利用有限的人力和财力资源。本文将讨论如何利用人工智能提高细菌分离的成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Improving the odds: Artificial intelligence and the great plate count anomaly

Next-generation DNA sequencing has shown that the great plate count anomaly, that is, the difference between bacteria present in the environment and those that can be obtained in culture from that environment, is even greater and more persisting than initially thought. This hampers fundamental understanding of bacterial physiology and biotechnological application of the unculture majority. With big sequence data as foundation, artificial intelligence (AI) may be a game changer in bacterial isolation efforts and provide directions for the cultivation media and conditions that are most promising and as such be used to canalize limited human and financial resources. This opinion paper discusses how AI is or can be used to improve the success of bacterial isolation.

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来源期刊
Microbial Biotechnology
Microbial Biotechnology Immunology and Microbiology-Applied Microbiology and Biotechnology
CiteScore
11.20
自引率
3.50%
发文量
162
审稿时长
1 months
期刊介绍: Microbial Biotechnology publishes papers of original research reporting significant advances in any aspect of microbial applications, including, but not limited to biotechnologies related to: Green chemistry; Primary metabolites; Food, beverages and supplements; Secondary metabolites and natural products; Pharmaceuticals; Diagnostics; Agriculture; Bioenergy; Biomining, including oil recovery and processing; Bioremediation; Biopolymers, biomaterials; Bionanotechnology; Biosurfactants and bioemulsifiers; Compatible solutes and bioprotectants; Biosensors, monitoring systems, quantitative microbial risk assessment; Technology development; Protein engineering; Functional genomics; Metabolic engineering; Metabolic design; Systems analysis, modelling; Process engineering; Biologically-based analytical methods; Microbially-based strategies in public health; Microbially-based strategies to influence global processes
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