Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification

Abstract

The microbiome of the gut is a complex ecosystem that contains a wide variety of microbial species and functional capabilities. The microbiome has a significant impact on health and disease by affecting endocrinology, physiology, and neurology. It can change the progression of certain diseases and enhance treatment responses and tolerance. The gut microbiota plays a pivotal role in human health, influencing a wide range of physiological processes. Recent advances in computational tools and artificial intelligence (AI) have revolutionized the study of gut microbiota, enabling the identification of biomarkers that are critical for diagnosing and treating various diseases. This review hunts through the cutting-edge computational methodologies that integrate multi-omics data-such as metagenomics, metaproteomics, and metabolomics-providing a comprehensive understanding of the gut microbiome's composition and function. Additionally, machine learning (ML) approaches, including deep learning and network-based methods, are explored for their ability to uncover complex patterns within microbiome data, offering unprecedented insights into microbial interactions and their link to host health. By highlighting the synergy between traditional bioinformatics tools and advanced AI techniques, this review underscores the potential of these approaches in enhancing biomarker discovery and developing personalized therapeutic strategies. The convergence of computational advancements and microbiome research marks a significant step forward in precision medicine, paving the way for novel diagnostics and treatments tailored to individual microbiome profiles. Investigators have the ability to discover connections between the composition of microorganisms, the expression of genes, and the profiles of metabolites. Individual reactions to medicines that target gut microbes can be predicted by models driven by artificial intelligence. It is possible to obtain personalized and precision medicine by first gaining an understanding of the impact that the gut microbiota has on the development of disease. The application of machine learning allows for the customization of treatments to the specific microbial environment of an individual.

Keywords: artificial intelligence (AI); gut microbiome; gut microbiota; machine learning (ML); network-based methods, biomarker discovery; personalized treatment; precision medicine.

Figure 1

A systematic approach for microbiome data analysis, covering the steps from raw reads to community analyses, incorporating multi-omics techniques and statistical models that allow for more accurate phenotypic and functional profiling.

Figure 2

A comprehensive and fascinating intersection of multi-omics, machine learning, databases and the gut microbiota showing evident synergy between multi-omics and machine learning that hold immense promise for advancing our understanding of the gut microbiota and its impact on human health.

Figure 3

Potential challenges and considerations in multi-omics and machine learning for advancing gut microbiome research.

Figure 4

Different computational, bioinformatics, AI & ML approaches in gut microbiome analysis and biomarker discovery for achieving D3 (diagnostics, discovery and decision) and goals of precision medicine.