Historically, our understanding of microbial communities relied on single-omics approaches. Targeted 16S rRNA gene sequencing provided essential taxonomic snapshots, answering which microorganisms are present in a given community, whilst shotgun metagenomics expanded this to clarify the genomic potential of the environment. However, single-omics studies possess inherent, systemic limitations. They cannot determine which genes are actively transcribed, which proteins are translated, or which biochemical phenotypes are ultimately realised. Biological processes are heavily regulated across multiple layers, and relying on a single omics layer offers an incomplete and often misleading view of the host-microbe interface. To overcome these limitations, the field has rapidly moved towards systems-level approaches that concurrently capture data from both the microbe and the host, finally allowing researchers to begin mapping the multi-layered causal networks that drive health and disease.

Yet the transition to multi-omics integration has introduced a monumental computational bottleneck. Multi-omics datasets are characterised by extreme high-dimensionality, with tens of thousands of features mapped across relatively small patient cohorts, inherent sparsity, high noise-to-signal ratios, and severe batch effects introduced by cross-platform variability. Traditional statistical frameworks, such as linear regressions or standard pairwise correlations, are fundamentally insufficient to handle these architectures. They struggle with non-linear biological dependencies, fail to scale efficiently, and are often blind to the emergent properties inherent in multi-layered biological networks. This computational crisis is precisely why artificial intelligence has emerged as an indispensable catalyst for modern microbiome research. Machine learning and deep learning architectures are uniquely suited to digest multi-dimensional, heterogeneous data, excelling at automated feature extraction, pattern recognition, and uncovering latent, non-linear relationships across disparate data types.

The biological basis of host-microbiome interactions is now being revealed with unprecedented technical depth. Amongst the most well-characterised metabolic axes are the short-chain fatty acid pathway, the bile acid pathway, and the tryptophan catabolism pathway. Short-chain fatty acids, principally acetate, propionate, and butyrate, are synthesised by intestinal microbiota through the anaerobic breakdown of non-digestible dietary carbohydrates in the large intestine, collectively contributing to host metabolism, immune regulation, and gut homeostasis. Bile acids, cholesterol-derived molecules synthesised in the liver, are converted by the gut microbiota into secondary bile acids through enzymatic reactions such as deconjugation, dihydroxylation, and oxidation, functioning as signalling molecules involved in lipid metabolism, glucose homeostasis, and immune regulation. Tryptophan, the least abundant canonical amino acid, serves as a precursor for serotonin, melatonin, and a large number of microbial and host metabolites, with its three major gastrointestinal catabolic pathways serving as a sensitive indicator of gut immune activation and microbial status.

Beyond metabolic crosstalk, host genetics significantly influence microbiome composition. Large-scale twin studies, genome-wide association studies, and microbiome-specific GWAS have progressively revealed how specific host single nucleotide polymorphisms govern microbial colonisation. Studies in Japanese and European populations have shown that variations in host genes can affect the abundance of specific gut microbes. Emerging evidence also points to a bidirectional epigenome-microbiome axis in which microbial metabolites reprogram host chromatin whilst host epigenetic states in turn influence the mucosal environment and microbial community composition. Short-chain fatty acids, particularly butyrate and propionate, regulate the activity of DNA methyltransferases, whilst microbiota-derived folate, methionine, and B vitamins contribute to the S-adenosylmethionine methyl donor pool that fuels methylation reactions. Non-coding RNAs, including microRNAs and long non-coding RNAs, represent an emerging cross-kingdom communication channel, with host-derived faecal miRNAs shown to enter bacterial cells and selectively modulate microbial gene expression.

On the technological front, several omics approaches are now available for analysing biological datasets. Metataxonomic sequencing, also known as 16S rRNA or amplicon sequencing, employs primers targeting conserved regions of hypervariable loops within bacterial ribosomal RNA genes, though its dependence on conserved target regions can restrict taxonomic resolution and PCR amplification may introduce biases and chimeric sequences. Whole metagenome sequencing sequences all DNA present within a sample, including both microbial and host genetic material, providing greater taxonomic and functional resolution but at high cost and analytical complexity. Metatranscriptomics captures transcriptionally active microbial taxa and their functional pathways, offering insights into dynamic community behaviour, though RNA is inherently less stable than DNA and host-derived sequences can complicate detection. Metaproteomics enables the simultaneous assessment of both microbial and host proteins, revealing how microbial proteins regulate host signalling pathways, immune modulation, and intestinal barrier integrity. Metabolomics unveils the small molecule composition, identifying metabolites that can originate from the host, microbes, or diet, with untargeted, semi-targeted, and targeted approaches each offering distinct trade-offs between coverage and quantitative rigour.

Integrating these heterogeneous datasets requires sophisticated computational frameworks. Techniques like Similarity Network Fusion allow the acquisition of complementary biological information across datasets by integrating sample similarity networks derived from different omics layers into a unified graphical representation. Multi-Omics Factor Analysis, a Bayesian latent factor model, uncovers the principal sources of variation within and between datasets, separating shared biological signals from those specific to individual omics platforms. Multiple Co-inertia Analysis identifies common patterns across different datasets by maximising covariance and projecting them into a shared dimensional space. Supervised approaches such as DIABLO leverage predefined phenotypic groups to identify correlated features across datasets whilst selecting key biomarkers associated with the phenotype of interest. However, the lack of standardised preprocessing protocols across different omics datasets creates difficulties in harmonisation, and the analysis of large heterogeneous datasets requires advanced expertise in bioinformatics, statistics, machine learning, programming, and biology.

Artificial intelligence is now actively driving predictive modelling of host-microbiome interactions across multiple disease domains. In inflammatory bowel disease, machine learning models incorporating baseline clinical, psychosocial, and lifestyle factors have been used to predict disease flares, whilst AI-assisted analysis of pan-enteric video capsule endoscopy has substantially reduced review time to 3.2 minutes per patient whilst maintaining 92 to 96 percent sensitivity for Crohn’s disease detection. In colorectal cancer, deep learning models have been developed to predict microsatellite instability from histopathological whole-slide images, achieving a negative predictive value of 93.7 percent on an external dataset and outperforming five experienced gastrointestinal pathologists. In metabolic syndrome, gradient boosting algorithms have achieved prediction accuracies of 93.1 percent in classifying metabolic components by integrating multi-omics datasets, whilst random forest models have demonstrated strong predictive performance for insulin resistance using transcriptomic and clinical data. Beyond prediction, AI is advancing therapeutic intervention, with computational platforms such as iProbiotics and metaProbiotics enabling the identification of probiotic strains from genomic and metagenomic data, and personalised dietary programmes using machine learning models significantly enhancing cardiometabolic health outcomes compared with conventional dietary recommendations.

Despite these advances, significant hurdles remain before AI-driven microbiome insights can be translated into clinical practice. Model generalisation, data interpretability, and methodological standardisation are persistent challenges, as few individual studies address more than one or two of the key analytical issues, including compositional data characteristics, ultra-high dimensional feature spaces, batch effect correction, zero-inflation dynamics, and multi-omics harmonisation, within the same pipeline. Rigorous head-to-head benchmarking of methods across shared datasets and evaluation criteria is still uncommon, making it difficult to draw general conclusions about which approach is genuinely superior for a given task. The high cost of sample collection and data generation also makes it difficult for individual studies to detect small but meaningful associations, underscoring the need for shared repositories of microbiome multi-omics data with careful attention to differences in data-processing methods across studies.

Looking ahead, the convergence of multi-omics with artificial intelligence, foundation models, and digital twin technologies is expected to drive the next generation of precision medicine. Recent advances in large-scale AI architectures offer exciting possibilities for generating comprehensive representations of host-microbiome interactions by integrating heterogeneous datasets across different biological scales. Furthermore, the incorporation of biological knowledge, such as metabolic pathways, microbial interaction networks, and host signalling cascades, into AI frameworks could enhance both predictive performance and biological interpretability. Predictive modelling of microbiome-derived metabolites, including short-chain fatty acids, bile acids, and tryptophan metabolites, may enable personalised prediction of metabolic responses, disease susceptibility, and therapeutic outcomes. As the field matures, robust validation models and standardised analytical pipelines will be essential to facilitate more accurate diagnostic approaches, predictive nutrition, and therapeutic strategies tailored to an individual’s microbiome composition and functional profile.

Subject of Research: AI and Multi-Omics Converge to Decode the Hidden Language of the Human Microbiome

Article Title: Decoding host–microbiome interactions through integrative multi-omics and artificial intelligence: from biological mechanisms to predictive modelling

Article References: Krishna S., G., Reeja, N., Sadanandan, S., Mappurath, B., Chatterjee, A., Antony, G., & Grace, T. (2026). Decoding host–microbiome interactions through integrative multi-omics and artificial intelligence: from biological mechanisms to predictive modelling. Frontiers in Microbiomes, 5, Article 1906484. https://doi.org/10.3389/frmbi.2026.1906484

Image Credits: AI Generated

DOI: 10.3389/frmbi.2026.1906484

Keywords: artificial intelligence, multi-omics, host-microbiome, machine learning, deep learning, precision medicine, gut-brain axis, metabolomics, inflammatory bowel disease, cancer, Decoding, host