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image of A Multipurpose Machine Learning Application in Microbiological Data

Abstract

Microorganisms are widespread and essential to the transformation of substances and organic matter. Researchers studied microorganisms through various conventional methods, such as machine learning (ML), to overcome multiple obstacles. This review aims to highlight the involvement of ML in various aspects of microbiology to provide insightful information, along with advancement challenges.

Concerning the microbiological aspects and the integration of ML and their associated applications, the relevant literature was diligently reviewed to collect meaningful information on the ML involvement in different fields of microbiology and discussed.

Due to the complexity of microbiological data, the researchers are using the amalgamation of various stages and diverse ML applications to deal with and organize the data systematics for accurate results and proper hypotheses. Subsequently, navigating these microbiological data requires an extensive feature-based model for the appropriate validation and to obtain accurate results.

This study mainly summarizes the various applications and development of ML models used in many aspects, especially the fundamentals of ML in microbiological data, clinical applications, microbial ecology, and the surrounding environment. At present, ML's involvement in microbial aspects is widely utilized; however, bulk data and proper information are needed for accurate and informative outcomes. This review sheds light on ML's involvement in microbiological aspects, and briefly discusses the different aspects. The advanced approaches followed by different tools and databases can be a potential lead toward significant research and promising findings.

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2025-08-21
2025-11-01

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/content/journals/cbio/10.2174/0115748936414246250808022341
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A Multipurpose Machine Learning Application in Microbiological Data
Bentham Science Publishers ; https://doi.org/10.2174/0115748936414246250808022341
/content/journals/cbio/10.2174/0115748936414246250808022341
/content/journals/cbio/10.2174/0115748936414246250808022341
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  • Article Type:     Review Article
Keywords: microbiology ; unsupervised learning ; microorganisms ; microbial ecology ; microbiome ; deep learning ; antimicrobial resistance ; Machine learning ; supervised learning

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