Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease

Abstract

The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe–disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review

“…The omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are widely used in the study of gut microbiome due to their ability to provide high-throughput and high-resolution data. The vast amount of data generated via these methods has led to the development of computational methods for data processing and analysis, which is a field where ML can be used as a powerful tool [ 108 ]. In this regard, ML provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, extrapolation of host phenotypes to predict diseases, and the use of microbial communities to stratify patients by the characterization of state-specific microbial signatures [ 109 ].…”

Gut Microbiota Are a Novel Source of Biomarkers for Immunotherapy in Non-Small-Cell Lung Cancer (NSCLC)

“…There are several reports about using ML and microbiota. From the data on the microbiota produced by omics-based methods (metagenomics, meta transcriptomics, and metabolomics), ML can predict and find new non-theoretically inferred information to help us to increase the efficacy and reduce iRAEs [58,59].…”

The future of artificial intelligence in clinical nutrition

“…The strength of this study was the use of the combination of neuroimaging data with microbiota giving a strong added value to the machine learning approach. However, the use of microbiota database has been associated with unmet challenges [13]. The authors recommend addressing these challenges by including the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks.…”