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image of Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods

Abstract

Background

Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient.

Objective

This study aims to discover essential differences for different intestinal bacteria.

Methods

This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 , 447 , and 796 , by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen , we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification.

Results

Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying , , and .

Conclusion

This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.

© 2025 Bentham Science Publishers
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2025-07-15
2025-09-13

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Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods
Bentham Science Publishers ; https://doi.org/10.2174/0115665232367064250630202337
/content/journals/cgt/10.2174/0115665232367064250630202337
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  • Article Type:     Research Article
Keywords: disease pathology ; ecosystem ; machine learning ; gut dysbiosis ; feature selection ; Gut bacteria

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