Introduction
Inflammatory bowel diseases (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), result from interactions between the host, environmental factors, and gut microbiome.1,2 A common feature of IBD is microbial community alteration, characterized by reduced microbial diversity, depletion of short-chain fatty acid–producing bacteria, enrichment of opportunistic taxa, and disrupted metabolic pathways that change with disease activity and therapeutic interventions.3–5 Recent longitudinal and multi-omics studies have shown that these changes in compositional and functional shifts are closely related to host transcriptomic, proteomic, and metabolomic signatures, underscoring the centrality of host–microbe interactions in disease pathogenesis.6–8
Despite these advances, significant clinical unmet needs remain. The diagnosis of IBD is often delayed because symptoms overlap with those of other gastrointestinal disorders,9,10 and current biomarkers such as C-reactive protein and fecal calprotectin lack disease specificity and prognostic utility.11–13 Reliable microbial or molecular biomarkers capable of predicting treatment response or relapse risk are still lacking, highlighting the need for integrative analytic frameworks that can bridge the gap between discovery and clinical application.14,15 Traditional statistical analyses, including univariate (Linear discriminant analysis Effect Size, LEfSe) or multivariate regression models (Microbiome Multivariable Association with Linear Models, MaAsLin2), have been instrumental in identifying differentially abundant taxa and host factors.16,17 However, these approaches are limited in capturing nonlinear interactions and complex dependencies within high-dimensional, multi-omics datasets.18,19
These datasets are intrinsically high-dimensional, sparse, and heterogeneous across cohorts, sampling sites, sequencing platforms, and geographies, with outcomes that can vary considerably depending on the analytical methods applied. Longitudinal and cross-cohort studies have revealed significant inter-individual variability and nonlinear dependencies among microbes, host factors, and metabolites, which makes finding biomarkers more complicated.7,20 Additionally, large-scale integration of metagenomic and metabolomic data has shown both the potential to identify disease-related features and the ongoing challenge of aligning results across studies. This emphasizes the need for strong computational methods to handle this complexity.6,21
In this context, machine learning (ML) has emerged as a robust framework for characterizing gut microbial changes in IBD.22 ML approaches are particularly well-suited for (i) extracting predictive patterns from high-dimensional data, (ii) integrating heterogeneous inputs spanning microbial taxa, functional genes, host omics, and clinical metadata, and (iii) delivering measurable classification performance for diagnostic, prognostic, and therapeutic stratification.23 Applications in IBD cohorts have demonstrated that ML models outperform traditional statistical methods in discriminating cases from controls, differentiating subtypes, and predicting treatment responses.4,24 Simultaneously, benchmarking studies and recent state-of-the-art reviews have emphasized critical challenges—including external validation, model calibration, interpretability, and transparent reporting—that must be addressed before ML-driven microbiome biomarkers can be reliably translated into clinical settings.18,23,25
Building on recent reviews that highlighted the importance of methodological robustness, reproducibility, and the need for clinical translation in AI-driven microbiome research,26,27 this review extends these perspectives by incorporating frameworks such as time-series and causal inference modeling, multi-omics data integration, and foundation-model approaches to enhance translational relevance. We provide a comprehensive overview of recent applications of ML for characterizing gut microbiome alterations in IBD, emphasizing commonly used algorithms, analytical pipelines, and model evaluation strategies. Finally, we highlight the diagnostic and prognostic potential of ML-based approaches and discuss their strengths, limitations, and key considerations in translating microbiome-derived insights into clinically applicable tools for precision medicine.
Microbiome Dysbiosis in IBD: From Traditional Studies to Multi-Omics Analyses
Early studies on gut microbiome in IBD primarily relied on 16S rRNA gene sequencing, which enabled cost-effective profiling of microbial communities at the genus or family level. These studies provided the first evidence of disease-associated dysbiosis characterized by reduced alpha diversity, enrichment of pathobionts, and depletion of commensals.3,5 Initial analytical approaches focused mainly on univariate comparisons of relative abundances, typically using methods such as LEfSe.17 Ma et al applied LEfSe analysis to compare patients with early-stage CD, advanced CD, and healthy controls, identifying enrichment of Parabacteroides and Lachnospiraceae incertae sedis in early CD, expansion of Escherichia-Shigella and Proteus in advanced CD, and preservation of short-chain fatty acid-producing taxa such as Roseburia and Butyricicoccus in healthy controls.28 These findings demonstrate how LEfSe highlights disease-stage-specific microbial signatures while underscoring its reliance on univariate contrasts.
However, subsequent microbiome analyses have increasingly adopted multivariate analyses that incorporated metadata-based adjustments and advanced statistical modeling. Tools, such as MaAsLin216/MaAsLin329 and ANCOM-BC30/ANCOM-BC2,31 further enhance feature selection by integrating clinical metadata and complex covariates. Chen et al used MaAsLin2 to identify taxa whose relative abundances differed in quiescent CD (CD-R), independent of active inflammation and environmental or genetic confounders.32 They found that Faecalibacterium, Dorea, and Fusicatenibacter were significantly decreased in patients with CD-R compared to healthy first-degree relatives and non-relative healthy controls. Rosso et al applied ANCOM-BC to fecal bacterial profiles of patients with IBD (both UC and CD) vs non-IBD controls in Buenos Aires.33 Among the bacterial genera that were differentially abundant in ANCOM-BC, Bifidobacterium was enriched in patients with UC compared to non-IBD controls, whereas in patients with CD, Bifidobacterium, Bacteroides, Lactobacillus, and Faecalibacterium were identified as differentially abundant taxa. Together, these approaches reduced the analytical bias and enabled a more reproducible investigation of disease-specific taxa, marking a substantial advancement from exploratory compositional analyses to clinically grounded biomarker discovery. In addition, other methods such as ALDEx234 and LinDA,35 although not discussed in detail in this review, represent parallel efforts to further mitigate the biases inherent to differential abundance analysis.
The advent of high-throughput shotgun metagenomic sequencing has provided a technological leap, offering strain-level resolution and direct access to microbial functional repertoires, including genes and pathways relevant to host metabolism and immunity.36 In a recent multibiome study, Akiyama et al applied shotgun metagenomic sequencing with MaAsLin2 across Japanese and external validation cohorts to characterize microbial signatures in IBD.37 Both UC and CD are associated with the depletion of short-chain fatty acid-producing bacteria and the enrichment of Enterococcus faecium and Bifidobacterium species. Notably, Escherichia coli specifically increased only in patients with CD, highlighting its potential role as a disease-specific microbial marker.
Importantly, gut microbiome dysbiosis in patients with IBD is not limited to bacteria. Studies on the gut virome38,39 have reported an increased abundance of bacteriophages (eg, Caudovirales) and decreased abundance of eukaryotic viruses, while mycobiome studies have identified altered fungal-bacterial interactions and increased abundance of Candida species.40 Beyond the microbial taxa themselves, metabolomic and proteomic analyses revealed disease-associated alterations in bile acids, short-chain fatty acids, and amino acid derivatives, providing functional readouts that link microbial activity to host immune and epithelial responses.41,42 Together, these multi-omics approaches provide a more comprehensive understanding of microbial changes and its systemic effects.
However, the integration of multiple data layers has resulted in significant analytical challenges. Microbiome and multi-omics datasets are intrinsically high-dimensional, sparse, and heterogeneous with marked variability across cohorts, sequencing platforms, and sample types.18,19,43 Traditional univariate and linear statistical approaches are often insufficient for capturing nonlinear dependencies and cross-modal interactions. Although analytical tools such as LEfSe and MaAsLin2 are widely used for differential abundance and association testing, they are inherently sensitive to compositional bias, differences in sequencing depth, and batch effects.19,44 LEfSe operates on relative abundances and may generate spurious associations when sample library sizes vary, whereas MaAsLin2 assumes linear relationships and is affected by zero inflation and normalization artifacts. These issues have been repeatedly highlighted in recent benchmarking studies, emphasizing the need for compositional data–aware methods and rigorous normalization to ensure cross-cohort reproducibility and biological validity.45 Collectively, these challenges have motivated the adoption of ML and related artificial intelligence (AI) approaches, which are particularly well-suited for recognizing complex patterns in high-dimensional data and enabling multimodal integration.46,47 Leveraging these advanced computational frameworks, researchers aim to move beyond traditional analyses toward more reproducible, predictive, and clinically interpretable models of gut microbiome dysbiosis in IBD.
Machine Learning Applications in IBD Microbiome Research
As the complexity of microbiome data in IBD has become more apparent, ML approaches have been increasingly adopted to address the limitations of conventional statistical analyses.18,48 Unlike traditional differential abundance methods, ML algorithms can handle high-dimensional and heterogeneous data and discover subtle nonlinear patterns that may better capture disease-associated microbial biomarkers.49,50 These shifts have established ML as a key tool for developing diagnostic and prognostic models of IBD.51,52
Disease Classification and Subtype Diagnosis
One of the earliest and most widely explored applications of ML in microbiome research was disease diagnosis. Classifier models trained on microbial abundance profiles or functional gene features have been used to discriminate patients with IBD from healthy controls,24 distinguish patients with CD from those with UC,50 and predict clinical outcomes such as flare risk, therapeutic response, or remission status.52,53 In many cases, ML classifiers have achieved significant performance as measured via receiver operating characteristic (ROC) curves, the area under the curves (AUCs), sensitivity, and specificity, often surpassing the predictive capacity of conventional biomarkers such as fecal calprotectin.24 For example, Liñares-Blanco et al applied a panel of supervised learning algorithms to fecal microbiome data and developed a robust signature capable of classifying not only IBD versus controls but also differentiating CD from UC.50 Their random forest (RF)-based model achieved high AUC values across cross-validation, and feature selection identified a reproducible set of bacterial taxa that contributed most strongly to classification. Similarly, Park et al reported that a fecal microbiome-based ML model accurately discriminated between CD and UC, further confirming that microbial signatures can serve as reliable classifiers of IBD subtypes.51 Recent large-scale studies have underscored the diagnostic power of microbiome-informed ML. Zheng et al’s multi-ethnic cohort analysis demonstrated that an ML classifier trained on microbial taxonomic features achieved AUCs exceeding 0.7~0.9, outperforming conventional biomarkers such as fecal calprotectin in identifying both UC and CD.24 Similarly, Kim et al constructed an ML model using Korean fecal metagenomes that reliably differentiated patients with IBD from healthy patients, emphasizing the generalizability of microbiome signatures across populations.54 Together, these studies highlight that microbiome-based ML frameworks may evolve into noninvasive diagnostic tools capable of complementing or reducing the need for invasive endoscopy.
Biomarker Discovery and Algorithmic Approaches
In addition to its classification capabilities, ML plays a key role in the discovery of disease-specific features and biomarkers.55,56 By ranking microbial taxa, genes, or metabolites according to their predictive importance, algorithms not only improve model interpretability but also generate hypotheses about how microbes might contribute to diseases. This capability has enabled the development of candidate biomarker panels that integrate microbial and metabolic features with clinical metadata, thereby providing a more comprehensive view of IBD pathophysiology. Among the various algorithms employed, RF57 is the most widely used in microbiome studies because of its robustness to noise, sparsity tolerance, and straightforward feature importance readouts.58 Knights et al highlighted its stability and performance compared to traditional statistical approaches.59 Kim et al further applied RF–based feature selection to identify microbial markers associated with IBD, distinguishing taxa such as Veillonella and Escherichia-Shigella between patients with UC and those with CD.54 Manandhar et al also applied RF classifiers trained on gut microbiome features to diagnose IBD and differentiate CD from UC using the American Gut Project data comprising 729 patients with IBD and 700 healthy patients.47 With 50 LEfSe-selected taxa, the RF model achieved a test AUCs of > 0.80 for IBD versus controls; using the top 500 high-variance operational taxonomic units (OTUs) further improved performance to an AUCs > 0.82. For subtype classification (331 CD vs 141 UC), RF models trained on either differential taxa or high-variance OTUs attained test AUCs > 0.90, underscoring their strong discriminatory power and potential clinical utility.
Although RF remains the most commonly used approach, other algorithms, including support vector machines (SVMs) and boosting frameworks (eg, XGBoost and AdaBoost), are also widely applied.60,61 In an extensive multiomics study of CD location subtypes, Gonzalez et al systematically evaluated ten different algorithms (including RFs, extra trees, decision trees, SVMs, multilayer perceptron classifiers, voting classifiers, naïve Bayes, k-nearest neighbors, logistic regression, and AdaBoost) for their ability to classify disease subtypes. The authors identified the best-performing models and applied them to downstream analyses, revealing distinct associations according to disease location. Specifically, colonic CD exhibited a greater similarity to ulcerative colitis and showed stronger associations with Bacteroides vulgatus and neutrophil activity, whereas ileal CD was more closely linked to bile acid metabolism and demonstrated marked alterations in Faecalibacterium prausnitzii. These findings underscore how both the model choice and feature modality substantially influence predictive performance.62
Besides single-algorithm approaches, ensemble methods are also advantageous. Ha et al trained eight ML algorithms, including RF, deep neural networks (DNNs), logistic regression, k-nearest neighbors, decision trees, gradient boosting, and SVMs, on multi-cohort pediatric IBD data. An ensemble model combining three top-performing classifiers (DNN, logistic regression, and SVMs) achieved the highest accuracy in predicting future remission, outperforming any single algorithm.52 The characteristics of each ML/DL algorithm are summarized in Table 1.
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Table 1 Comparative Characteristics and Performance Trends of ML Algorithms in IBD Microbiome Research
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Challenges and Limitations of ML in IBD Microbiome Research
However, despite these advances, several challenges remain unresolved. Overfitting is a recurrent issue due to the imbalance between high-dimensional microbial features and relatively small sample sizes. A recent review by Dudek et al emphasized the risks of data leakage and overfitting in microbiome ML workflows, calling for more rigorous cross-validation and model-locking strategies.69 In addition to overfitting, performance metrics such as AUCs or accuracy often rely solely on internal cross-validation and may overestimate true generalizability. Robust evaluation requires nested cross-validation, bootstrapping, and, ideally, independent external validation to ensure fair model assessment and prevent information leakage.48,70 As demonstrated by Kubinski et al, model performance varies markedly across preprocessing choices and cohorts, underscoring the difficulty of achieving cross-cohort reproducibility.71 Moreover, upstream sources of technical noise—including differences in normalization procedures, zero-inflated feature distributions, and the inherent sparsity and compositionality of microbial data—can substantially distort model behavior and feature importance rankings, further complicating reproducibility and external validation.71–73 Furthermore, as highlighted by Papoutsoglou et al, the lack of standardized preprocessing pipelines and benchmarking datasets undermines reproducibility and complicates comparisons across studies.48 Collectively, these findings demonstrate that while ML has brought substantial progress beyond traditional statistical approaches in IBD microbiome research, persistent barriers, including overfitting, limited generalizability, and lack of methodological standardization, must be addressed. Rigorous external validation, harmonized data pipelines, and the integration of interpretable ML frameworks are essential to ensure that microbiome-based models can transition into reliable clinical applications.74
Emerging Approaches Beyond Traditional ML
While traditional ML approaches such as RF and SVMs have shown strong utility in IBD microbiome research, recent years have seen the rise of deep learning (DL) and next-generation AI frameworks that aim to capture even more complex and nonlinear relationships within multi-omics data.66,75 Unlike traditional methods that often rely on predefined or engineered features, DL methods use representation learning to automatically extract latent patterns from raw or minimally processed inputs.75,76 Oh and Zhang reported DeepMicro as a notable early framework that employed autoencoders to compress high-dimensional microbiome profiles into compact latent representations, thereby enhancing the disease classification performance. Building on this foundation, subsequent DL applications have explored a range of architectures: autoencoders for dimensionality reduction and feature learning,75 and convolutional neural networks (CNNs) to identify structured patterns in microbial abundance matrices.77 More advanced models, such as the Multimodal Variational Information Bottleneck (MVIB), further integrate multi-omics layers and clinical metadata, highlighting how DL can support more accurate and clinically relevant predictions.78 Collectively, these approaches emphasize the potential of DL to surpass conventional ML, offering scalable and flexible tools for predictive microbiome research.
Recently, attention has shifted toward the development of foundation models that leverage large-scale pretraining on biological sequences to generate generalized representations. The Microbial General Model (MGM) was one of the first attempts to construct a microbiome-focused foundation model trained on more than 260,000 microbiome samples using transformer-based pretraining.79 Although still in its early stages, the MGM demonstrates how models tailored to microbial communities can eventually classify community types, identify keystone taxa, and capture longitudinal dynamics, thereby providing a scalable framework for studying microbiome-driven processes in IBD. In parallel, host-centric genome foundation models, such as AlphaGenome80 and Nucleotide Transformer,81 have been developed to annotate genetic variants and predict their regulatory effects in human genomics. Although not originally designed for microbial applications, these host-oriented models provide transferable embeddings that may help bridge host genetic information with microbial-community dynamics. Together, microbiome- and host-focused foundation models highlight a forward-looking trajectory in which unified representational spaces may enable multi-omics integration and deepen our understanding of host–microbiome interactions in IBD.
In parallel, recent multi-omics studies have begun to adopt time-series and causal inference models that capture longitudinal microbiome–host interactions and reveal potential drivers of disease dynamics.82,83 Dynamic Bayesian networks, Granger-causality analysis, and structural causal modeling frameworks allow researchers to infer temporal dependencies and directionality in microbial and metabolic shifts, providing insights that static cross-sectional analyses cannot capture.84 These temporal and causal modeling approaches are crucial for understanding how microbial community states evolve during inflammation, remission, and therapeutic intervention in IBD. In addition, multi-omics data-fusion frameworks have rapidly advanced predictive microbiome analytics by integrating heterogeneous data layers within shared latent spaces. Representative examples include Multi-Omics Factor Analysis (MOFA+), which applies probabilistic factor modeling to uncover coordinated variance across omics layers;85 DIABLO, a multivariate approach for discriminative integration of multi-omic modalities;86 and deep representation learning frameworks, such as variational autoencoder–based or multimodal transformer architectures, which embed genomic, transcriptomic, and metabolomic profiles into unified representations for improved disease prediction and mechanistic interpretation.75 More recently, the MintTea (Multi-omics Integration through Temporal Embedding and Attention) framework has extended this concept by integrating longitudinal multi-omics data through attention-based deep learning, enabling the identification of dynamic, disease-associated molecular modules across time and modalities.87 These integrative frameworks exemplify the shift from isolated single-omic analysis toward data-fusion models that bridge microbial, metabolic, and host dimensions of IBD.
As these models grow in complexity, concerns regarding their interpretability have become more pressing. To ensure clinical relevance and acceptance, the integration of explainable AI (XAI)88 methods, such as SHapley Additive exPlanation (SHAP) values, attention weight visualization, and concept bottleneck models, has been proposed.89 For instance, Novielli et al applied SHAP values to microbiome classification tasks and showed that taxa such as Fusobacterium and Parvimonas were key drivers of colorectal cancer predictions, thereby providing transparent links between features and model outputs.90 Similarly, Onwuka et al demonstrated that the SHAP-based prioritization of fecal and plasma metabolites could highlight IBD-associated signatures, reinforcing the role of XAI in biomarker discovery and clinician application.91 In line with these advances, a recent review by Kim et al emphasized that the field is shifting from descriptive profiling of dysbiosis toward more predictive and analytic (including prescriptive) applications, while highlighting ongoing challenges such as biological and technical heterogeneity, limited generalizability across cohorts, and the need for methodological standardization and external validation.92
Clinical Translation and Challenges
The application of ML and DL to the IBD microbiome data has generated promising diagnostic models and candidate biomarker panels.93,94 However, the path from computational discovery to clinical implementation remains complex and requires careful consideration of several limitations.95,96 While traditional statistical analyses and ML/DL-based approaches have successfully identified bacterial taxa associated with disease states, causal or mechanistic links between these microbes and host physiology have been established only for a limited subset. Most computational analyses cannot by themselves determine whether these microbial signatures drive, result from, or merely correlate with inflammation. For example, several genera including Faecalibacterium frequently identified by differential abundant taxa analysis are known to produce short-chain fatty acids that modulate intestinal immune responses, and their depletion may contribute to mucosal inflammation and barrier dysfunction in IBD.97,98 Conversely, enrichment of Escherichia–Shigella or Enterococcus may reflect pro-inflammatory activity through lipopolysaccharide-mediated TLR signaling.99,100 To validate and interpret these associations, further experimental confirmation through in vivo or ex vivo studies is required, as well as the use of next-generation computational frameworks—such as foundation-model-based simulation or causal network modeling—to infer and test host–microbe interaction dynamics.101
Standardization and Reproducibility of Microbiome Assays
A key challenge is the standardization of microbiome assays. Pre-analytical variables such as sample types (feces or biopsies), stool collection methods, preservation techniques, sequencing platforms, and library preparation introduce substantial variability that directly affects downstream feature profiles.
In particular, differences in sequencing chemistry, read length, and bioinformatic preprocessing pipelines can introduce systematic biases in taxonomic resolution and relative abundance estimation.102,103 Batch effects arising from DNA extraction kits, primer selection, and library preparation protocols have been shown to exceed inter-individual biological variability in some cases, thereby confounding disease-associated microbial signatures across studies.104
Together, these technical factors interact with sampling- and host-related sources of variability, underscoring the importance of methodological harmonization. Kim et al compared the gut microbiome profiles obtained from the stool, luminal contents, and mucosal biopsies of patients with UC and healthy controls.105 They found significant differences in both alpha and beta diversities across sample types, with biopsies yielding higher numbers of observed OTUs than stool or lavage samples. Community structures showed a correlation between stool and luminal contents, whereas biopsy samples did not correlate with either. Importantly, UC-control differences were evident only in stool samples, whereas lavage and biopsy samples did not show significant separation, partly due to the limited sample size. Classification analysis achieved AUCs of 0.85 for stool and 0.81 for lavage, underscoring that the sampling strategy critically influences microbiome readouts and diagnostic performance. Kruger et al systematically assessed intra- and inter-individual variability in gut health markers in healthy adults using an optimized fecal sampling and processing workflow.106 They showed that the variability differed by marker, with some exhibiting substantial within-person variations. Importantly, they demonstrated that optimized pre-processing methods, such as mill homogenization, could significantly reduce technical variability, highlighting that methodological choices can obscure biological signals if not standardized. This underscores the need for assay harmonization to ensure reliable biomarker discovery and provide a robust foundation for ML applications in clinical contexts. Similarly, Clooney et al performed a large longitudinal multicenter study across two continents in patients with IBD and controls, revealing that the geographic site and temporal factors explained a significant share of the microbiome variance, often rivaling or exceeding disease effects.107 They further observed that temporal stability was reduced in IBD, and that model performance improved when consecutive time points were considered. These findings illustrate how cross-site and temporal heterogeneity can mask disease-associated signatures, and emphasize the importance of standardized methodologies and rigorous external validation in the development of ML-based diagnostics and prognostics for IBD. Unless these sources of heterogeneity are rigorously controlled, models trained in one context are unlikely to be generalized to other clinical settings.108 This challenge becomes even more pronounced when integrating multi-omics layers, such as metabolomic, transcriptomic, and proteomic data, which adds further complexity to model development and validation.
Model Interpretability and Explainable AI
Another major concern was the interpretability of the model. Algorithms such as RF, boosting algorithms, and neural networks often achieve high predictive performance but can act as “black boxes”.96 In clinical practice, the opacity of AI models can undermine the trust of clinicians and complicate regulatory approval. A systematic review by Rosenbacke et al reported that explainable AI can either enhance or diminish trust depending on how explanations are presented.109 Explanations that are clear, concise, and consistent with clinical reasoning tend to increase clinicians’ confidence in AI systems. Conversely, explanations that are overly technical, complex, inconsistent with clinical logic, or misleading can reduce it. Thus, providing transparent and clinically relevant explanations is essential for successfully integrating AI into healthcare and securing the trust of medical professionals. Rynazal et al applied XAI methods (notably SHAP for local explanations) to colorectal cancer (CRC) classification using gut microbiome data.110 They demonstrated that using local (patient-specific) feature attributes revealed which microbial taxa were most influential for individual predictions and enabled the stratification of patients with CRC into subgroups, thereby improving interpretability and potentially pointing toward mechanistic hypotheses. Yu et al developed a 10-species microbial signature for IBD using an XGBoost-based model (termed XGB-IBD10) and performed external validation.111 Importantly, they applied SHAP analysis to explain model predictions, showing how individual taxa influenced the classification decisions. External validation confirmed the robustness of the identified microbial panel, demonstrating that explainability could enhance trust in model outputs and provide mechanistic insights into disease-associated microbes.
Taken together, these studies illustrate that XAI approaches, including feature importance ranking, SHAP values, and attention-based visualization, are critical not only for identifying which microbial taxa, metabolic pathways, or host features drive predictions but also for facilitating clinical adoption and guiding biological hypothesis generation. This process can provide a reliable output for clinical deployment.
Demonstrating Clinical Utility and Path to Adoption
Finally, the demonstration of its clinical utility remains a missing link. Current studies primarily focus on metrics such as the AUC, sensitivity, and specificity. Although these benchmarks are essential for the initial validation, they fail to capture whether such models can improve patient care. To achieve regulatory approval and widespread clinical uptake, predictive frameworks must demonstrate tangible benefits, such as reducing unnecessary colonoscopies, expediting the time to therapy initiation, and lowering healthcare costs. Ultimately, prospective validation and interventional studies are required to bridge the gap between computational promise and real-world clinical impact.
Recent longitudinal research has underscored this need. Al Radi et al investigated the predictive value of gut microbiome signatures for therapy intensification in IBD using a 10-year follow-up cohort.112 Their findings demonstrated that baseline microbial features could predict the need for treatment escalation, highlighting the importance of long-term monitoring and the value of microbiome-informed risk stratification for guiding clinical decision making. This study illustrated how longitudinal datasets provide a rigorous framework for evaluating the durability and clinical relevance of microbiome-derived predictors. In parallel, Massaro et al presented the design of the OPTIMIST study, a prospective, longitudinal, observational pilot study conducted in Canada to investigate gut microbiome predictors of advanced therapy response in CD.113 The study enrolled patients initiating biological treatment, as well as non-IBD controls, to link baseline microbiome composition and temporal dynamics to therapeutic outcomes. By prospectively monitoring patients over time, the OPTIMIST study established a framework for evaluating microbiome-based predictors in real-world settings and supporting their eventual clinical translation.
In summary, ML and DL hold considerable promise for translating microbiome research into diagnostic and prognostic tools for IBD. However, clinical translation will require overcoming persistent challenges related to reproducibility across cohorts, generalizability to diverse patient populations, interpretability of results, and addressing data-sharing constraints. Progress in explainable AI, development of standardized pipelines, and adherence to rigorous reporting and evaluation guidelines will be crucial in bridging the gap between proof-of-concept studies and real-world patient care.
Conclusion and Future Perspectives
ML and DL have transformed IBD microbiome research from descriptive community profiling to predictive modeling. Alongside DA analyses, these approaches have enabled the classification of disease versus health, the differentiation of CD from UC, and the identification of microbial and metabolic biomarkers with diagnostic and prognostic significance (Figure 1). Studies employing RF, boosting frameworks, and ensemble models have demonstrated that microbiome-informed classifiers can approach or even surpass traditional biomarkers, and explainable AI methods are beginning to provide clearer insights into the microbial and functional drivers of diseases.
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Figure 1 Evolution of analytical approaches for characterizing gut microbiota alterations in inflammatory bowel disease (IBD). An overview of analytical strategies for IBD illustrates the progression from traditional statistics to next-generation artificial intelligence (AI) frameworks. Early differential abundance (DA) analyses using 16S rRNA or metagenomic data identified disease-associated taxa; however, these were often limited by univariate contrasts and reproducibility issues. With the increasing data complexity of multi-omics approaches, machine learning (ML) approaches such as random forests, support vector machines (SVMs), and boosting algorithms have enabled robust disease classification, biomarker panel identification, and performance evaluation through receiver operating characteristic (ROC) curves, cross-validation, and feature importance scores. Recently, deep learning (DL) frameworks have been applied to capture nonlinear relationships in high-dimensional multi-omics datasets, providing latent feature embeddings and integrative predictive models. Looking ahead, foundation models trained on large-scale host and microbial genomic resources (eg, Microbial General Model for microbiome and AlphaGenome for host genetics) are emerging as transferable backbones that can be fine-tuned for diverse downstream tasks, from diagnosis and subtype prediction to therapy response modeling and prognosis.
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Despite this progress, significant barriers remain before clinical adoption can be achieved. Variability in stool collection, sequencing platforms, and preprocessing pipelines continues to hinder reproducibility, and external validation often reveals a decline in model performance when applied across independent cohorts. Dependence on complex “black-box” algorithms also makes it more difficult for clinicians to trust and regulators to approve. Furthermore, most studies are retrospective, with few prospective or interventional studies directly evaluating whether microbiome-based ML models can improve patient outcomes. These issues emphasize the need to connect computational advances with the development of practical tools for precision medicine.
Looking ahead, progress will be fastest if efforts focus on: (1) rigorous validation, prioritizing pre-registered pipelines, locked models, and multi-site external testing; (2) strong benchmarking, with standardized preprocessing, shared baselines, and challenge datasets covering stool and mucosal samples, platforms, and regions; (3) biologically based interpretability, pairing explanation methods with wet-lab or orthogonal assays to verify mechanistic plausibility; (4) longitudinal and multimodal integration, combining metagenomics, metabolomics, proteomics, and host features to model disease trajectories rather than single time points; and (5) next-generation AI, where emerging genome foundation models—both microbiome-focused and host-centric—are developed under strict evaluation and compared directly to robust classical baselines. These steps will help ML move from promising prototypes to dependable clinical tools that guide diagnosis, monitoring, and treatment decisions for IBD.
Data Sharing Statement
Data sharing not applicable – no new data generated.
Acknowledgments
The figure was generated using BioRender by June-Young Lee (2025) https://BioRender.com/je7gx57. We would like to thank Editage for the English language editing.
Author Contributions
- June-Young Lee (J.Y. Lee): Conceptualization; Methodology; Formal analysis; Software; Data curation; Investigation; Resources; Visualization; Funding acquisition; Writing – original draft; Writing – review & editing.
- Dong Hyun Kim (D.H. Kim): Formal analysis; Software; Validation; Data curation; Visualization; Writing – review & editing.
- Jee-Won Choi (J.W. Choi): Data curation; Investigation; Visualization; Writing – review & editing.
- Minho Shong (M. Shong): Resources; Investigation; Writing – review & editing.
- Chang Kyun Lee (C.K. Lee): Conceptualization; Methodology; Supervision; Project administration; Funding acquisition; Writing – review & editing.
All authors contributed substantially to the work, critically revised the manuscript, and agree to be accountable for all aspects of the work. All authors gave final approval of the version to be published. June-Young Lee and Dong Hyun Kim contributed equally to this work and share first authorship.
Funding
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023-KH135855) and the InnoCORE program of the Ministry of Science and ICT, Republic of Korea (grant number: N10250153).
Disclosure
The authors declare that they have no competing interests.
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