Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning
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Published:2023-11-17
Issue:22
Volume:24
Page:16459
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ISSN:1422-0067
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Container-title:International Journal of Molecular Sciences
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language:en
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Short-container-title:IJMS
Author:
Angelova Irina Y.1, Kovtun Alexey S.1ORCID, Averina Olga V.1, Koshenko Tatiana A.1, Danilenko Valery N.1ORCID
Affiliation:
1. Vavilov Institute of General Genetics, Russian Academy of Sciences (RAS), 119333 Moscow, Russia
Abstract
In the last few years, investigation of the gut–brain axis and the connection between the gut microbiota and the human nervous system and mental health has become one of the most popular topics. Correlations between the taxonomic and functional changes in gut microbiota and major depressive disorder have been shown in several studies. Machine learning provides a promising approach to analyze large-scale metagenomic data and identify biomarkers associated with depression. In this work, machine learning algorithms, such as random forest, elastic net, and You Only Look Once (YOLO), were utilized to detect significant features in microbiome samples and classify individuals based on their disorder status. The analysis was conducted on metagenomic data obtained during the study of gut microbiota of healthy people and patients with major depressive disorder. The YOLO method showed the greatest effectiveness in the analysis of the metagenomic samples and confirmed the experimental results on the critical importance of a reduction in the amount of Faecalibacterium prausnitzii for the manifestation of depression. These findings could contribute to a better understanding of the role of the gut microbiota in major depressive disorder and potentially lead the way for novel diagnostic and therapeutic strategies.
Funder
Russian Science Foundation
Subject
Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis
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