Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning

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.

Human Gut Microbiota for Diagnosis and Treatment of Depression

“…ML algorithms can also be used for the whole metagenome data, which not only describe the taxonomic abundance but also include information about the genes and metabolic pathways. In a recent work, it was demonstrated how the ML approach can be applied to develop a toolkit for in silico diagnosis of depressive disorder based on the biomarkers of the gut microbiota [ 130 ]. The classifications were made based on metagenomic signatures.…”