<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem.</p>
<p>Classification and prediction have emerged as key avenues for advancing microbial community research in computational biology. This research compares the advantages and disadvantages of using different algorithmic approaches in four subfields of microbiology (pathogen and epidemiology; microbial ecology; drug development; microbiome and taxonomy).</p>
</abstract>

Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated biologists. Microbes play a crucial role in the material and elemental cycles of the natural world. Growing own microbes for research purposes requires a significant time and financial investment. On the other hand, high-throughput sequencing technology cannot advance at the same clip as the culture method. The area of microbiology has made substantial use of machine learning (ML) methods to tackle this problem.
Classification and prediction have emerged as key avenues for advancing microbial community research in computational biology. This research compares the advantages and disadvantages of using different algorithmic approaches in four subfields of microbiology (pathogen and epidemiology; microbial ecology; drug development; microbiome and taxonomy).

Utility of Machine Learning Technology in Microbial Identification: A Critical Review

Advancements of Microbiology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Microorganisms are ubiquitous and have far-reaching effects on human life. Since their discovery in the 19th century, microorganisms have fascinated...

Studies	Availability of data and materials
Zhou et al.	https://www.nature.com/articles/ncomms5212#supplementary.information
KATZHMDA	http://dwz.cn/4oX5mS
BMCMDA	https://github.com/JustinShi2016/ISBRA2017

Utility of Machine Learning Technology in Microbial Identification: A Critical Review

Online veröffentlicht: 26. Juni 2023

Seitenbereich: 65 - 74

Eingereicht: 01. Okt. 2022

Akzeptiert: 01. Feb. 2023

DOI: https://doi.org/10.2478/am-2023-0006

Schlüsselwörter
Identification and classification, machine learning, microbial community

© 2023 Alok Bharadwaj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Fig. 2.

Fig. 3.

Web links of Microbiome – Disease Association

Utility of Machine Learning Technology in Microbial Identification: A Critical Review

Online veröffentlicht: 26. Juni 2023

Seitenbereich: 65 - 74

Eingereicht: 01. Okt. 2022

Akzeptiert: 01. Feb. 2023

DOI: https://doi.org/10.2478/am-2023-0006

SchlüsselwörterIdentification and classification, machine learning, microbial community

© 2023 Alok Bharadwaj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1.

Fig. 2.

Fig. 3.

Web links of Microbiome – Disease Association

Schlüsselwörter
Identification and classification, machine learning, microbial community