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AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome

Nature Medicine volume 31, pages 2991–3001 (2025)Cite this article

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Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data and detailed clinical symptoms. By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct a unique connectivity map spanning the microbiome, immune system and plasma metabolome in health and ME/CFS adjusted for age, gender and additional clinical factors. This map uncovers altered associations between microbial metabolism (for example, short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFN-γ and GzA. Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing unique mechanisms—specifically, how multi-omics dynamics are associated to the disease’s heterogeneous symptoms.

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Fig. 1: Cohort summary and heterogeneity of ME/CFS.
Fig. 2: BioMapAI’s model structure and performance.
Fig. 3: BioMapAI identifies both disease- and symptom-specific biomarkers.
Fig. 4: Microbiome-immune-metabolome crosstalk is dysbiotic in ME/CFS.
Fig. 5: Overview of dysbiotic host–microbiome interactions in ME/CFS.

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Data availability

All individual-level data supporting the findings of this study are publicly available. The complete dataset used in this study, including clinical metadata (including demographic information), immune profiles, plasma metabolomics, Quest Diagnostics blood measurements, and processed microbiome data, is available on GitHub: https://github.com/ohlab/BioMapAI/blob/main/data/Omics_Dataset.zip. Raw gut metagenomic sequencing data have been deposited in BioProject: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1125469.

Code availability

BioMapAI framework and user guide is available at https://github.com/ohlab/BioMapAI. All original code, processed data and trained model of this study has been deposited at Github.

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Acknowledgements

We are thankful to the Oh, Unutmaz and Li laboratories for inspiring discussions and acknowledge the contribution of the Genome Technologies Service at The Jackson Laboratory for expert assistance with sample sequencing for the work described in this publication. We also thank the clinical support team at the Bateman Horne Center and all the individuals who participated in this study. This work was funded by 1U54NS105539. R.X., E.A., R.C., S.D.V., L.K., C.G., L.B., D.U. and J.O. were funded by 1U54NS105539. J.O. is additionally supported by the NIH (1 R01 AR078634-01, DP2 GM126893-01, 1 U19 AI142733, 1 R21 AR075174).

Author information

Authors and Affiliations

  1. Duke University, Durham, NC, USA

    Ruoyun Xiong & Julia Oh

  2. The University of Connecticut Health Center, Farmington, CT, USA

    Ruoyun Xiong & Courtney Gunter

  3. The Jackson Laboratory, Farmington, CT, USA

    Ruoyun Xiong, Elizabeth Aiken, Ryan Caldwell, Lina Kozhaya, Courtney Gunter, Derya Unutmaz & Julia Oh

  4. Bateman Horne Center, Salt Lake City, UT, USA

    Suzanne D. Vernon & Lucinda Bateman

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Contributions

Conceptualization was carried out by D.U., J.O., S.D.V., L.B. and R.X. Data curation was performed by R.X., C.G., S.D.V. and L.B. Formal analysis was conducted by R.X. Funding acquisition was secured by D.U., J.O., S.D.V. and L.B. Clinical sample design and collection were conducted by S.D.V. and L.B. Investigation was carried out by R.X., C.G., E.A., S.D.V., L.B., R.C. and L.K. Project administration was handled by J.O., D.U., L.B., S.D.V. and C.G. Resources were provided by D.U., J.O., S.D.V. and L.B. Supervision was provided by J.O. Visualization and writing were completed by R.X. and J.O. Writing, review and editing were performed by R.X., C.G., S.D.V., L.B., D.U. and J.O.

Corresponding author

Correspondence to Julia Oh.

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Competing interests

S.D.V. is affiliated and has a financial interest with The BioCollective, a company that provided the BioCollector, the collection kit used for at-home stool collection discussed in this paper. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Data Pairedness Overview and Heterogeneity in Healthy and Patients.

a, Cohort Composition and Data Collection. Over four years, 515 time points were collected: baseline year from all 249 donors (Healthy N=96, ME/CFS N=153); second year from 168 individuals (Healthy N=58, ME/CFS N=110); third year from 94 individuals (Healthy N=13, ME/CFS N=81); fourth year from N=4 ME/CFS patients. Clinical metadata and blood measures were collected at all 515 points. Immune profiles from PBMCs were recorded at 489 points, microbiome data from stool samples at 479 points, and plasma metabolome data at 414 points. A total of 1,471 biosamples were collected. b-c, Heterogeneity of b, Healthy Controls and c, All Patients in Symptom Severity and omics Profiles. Supplemental information for Fig. 1b, which shows examples from 20 patients. Variability in symptom severity (top) and omics profiles (bottom) for all healthy controls and all patients with 3-4 time points. The top x-axis numbers represent 12 symptoms, arranged in the same order as Extended Data Fig. 1f, g (left to right, top to bottom). d, Distribution of 12 Clinical Symptoms in ME/CFS and Control. Density plots compare the distributions of 12 clinical scores between control (blue) and ME/CFS patients (orange) with the x-axis represents the values of symptom severity (scaled from 0%, no symptom, to 100%, most severe) and the y-axis represents the frequency (count) of data points. e, Principal Coordinates Analysis (PCoA) of each ‘Omics. PCoA based on Bray-Curtis distance. Control samples (blue) and ME/CFS patients (red) show distinct clustering. Here, except for the clinical scores, controls are indistinguishable from patients, highlighting the difficulty of building classification models. f-g, Symptom Progression Over Time in f, Healthy vs. g, ME/CFS Patients. Symptom progression for each individual (represented by different colors) is shown using line plots of symptom severity (y-axis) over time points (years 1–4). Compared to healthy controls, ME/CFS patients exhibit higher severity, greater heterogeneity, and inconsistent or nonlinear progression (indicated by substantial variation over time without a consistent pattern) in clinical symptoms. Related to: Figs. 1–2.

Extended Data Fig. 2 BioMapAI’s Performance at Clinical Score Reconstruction and Disease Classification.

a, Density map of True vs. Predicted Clinical Scores. Supplemental information for Fig. 2b, which shows three examples. Here, the full set of 12 clinical scores compares the true score, \(y\) (Column 1), against BioMapAI’s predictions generated from different omics profiles – \({\hat{y}}_{{immune}}\), \({\hat{y}}_{{species}}\), \({\hat{y}}_{{KEGG}}\), \({\hat{y}}_{{metabolome}}\), \({\hat{y}}_{{quest}}\), \({\hat{y}}_{{omics}}\) (Columns 2–7). b, Scatter Plot of True vs. Predicted Clinical Scores. Scatter plots display the relationship between true clinical scores (x-axis) and predicted clinical scores (y-axis) for six different models: Omics, Immune, Species, KEGG, Metabolome, and Quest Labs. Each plot demonstrates the clinical score prediction accuracy for each model. c, ROC Curve for Disease Classification with Original Clinical Scores. The Receiver Operating Characteristic (ROC) curve evaluates the performance of disease classification using the original 12 clinical scores. The mean Area Under the Curve (AUC) is 0.99, indicating high prediction accuracy, which aligns with the clinical diagnosis of ME/CFS based on key symptoms. d, 3D t-SNE Visualization of Hidden Layers. 3D t-SNE plots show how BioMapAI progressively distinguishes disease from control across hidden layers for five trained ‘omics models: Immune, KEGG, Species, Metabolome, and Quest Labs. Each plot uses the first three principal components to show the spatial distribution of control samples (blue) and ME/CFS patients (red). The progression from the input layer (mixed groups) to Hidden Layer 3 (fully separated groups) illustrates how BioMapAI progressively learns to separate ME/CFS from healthy controls. Related to: Fig. 2.

Extended Data Fig. 3 Disease-Specific Biomarkers - Top 10 Biomarkers Shared across Clinical Symptoms and Multiple Models.

Through the top 30 high-ranking features for each score, we discovered that the most critical features for all 12 symptoms were largely shared and consistently validated across ML and DL models, particularly the foremost 10. Here, this multi-panel figure presents the top 10 most significant features identified by BioMapAI across five omics profiles, highlighting their importance in predicting clinical symptoms and diagnostic outcomes across BioMapAI, DNN, and GBDT models, along with their data prevalence. Each vertical section represents one omics profile, with columns of biomarkers ordered by average feature importance from right to left. From top to bottom: 1. Heatmap of SHAP Values from BioMapAI. This heatmap shows averaged SHAP values with the 12 scores on the rows and the top 10 features in the columns. Darker colors indicate a stronger impact on the model’s output; Consistency among the top 5 features suggests they are shared disease biomarkers crucial for all clinical symptoms; 2. Swarm Plot of SHAP Values from DNN. This plot represents the distribution of feature contributions from DNN, which is structurally similar to BioMapAI but omits the third hidden layer (\({Z}^{3}\)). SHAP values are plotted vertically, ranging from negative to positive, showing each feature’s influence on prediction outcomes. Points represent individual samples, with color gradients denoting actual feature values. For instance, Dysosmobacteria welbionis, identified as the most critical species, shows that greater species relative abundance correlates with a higher likelihood of disease prediction; 3. Bar Graphs of Feature Importance in GBDT. GBDT is another machine learning model used for comparison. Each bar’s height indicates a feature’s significance within the GBDT model, providing another perspective on the predictive relevance of each biomarker; 4. Heatmap of Normalized Raw Abundance Data. This heatmap compares biomarker prevalence between healthy and disease states, with colors representing z-scored abundance values, highlighting biomarker differences between groups. Supporting Materials: Extended Data Table 3. Related to: Fig. 3.

Extended Data Fig. 4 Symptom-Specific Biomarkers - Immune, KEGG and Metabolome Models.

By linking omics profiles to clinical symptoms, BioMapAI identified unique symptom-specific biomarkers in addition to disease-specific biomarkers (Extended Data Fig. 3). Each omics has a circularized diagram (Fig. 3a, Extended Data Fig. 4b–d) to display how BioMapAI use this omics profile to predict 12 clinical symptoms and to discuss the contribution of disease- and symptom-specific biomarkers. Detailed correlation between symptom-specific biomarkers and their corresponding symptoms is in Extended Data Fig. 5. a, Examples of Sleeping Problem-Specific Species’ and Gastrointestinal-Specific Species’ Contributions. Supplemental information for Fig. 3d, which shows the contribution of pain-specific species. b-d, Circularized Diagram for Immune, KEGG and Metabolome Models. Supplemental information for Fig. 3a, which shows the species model. e-f, Zoomed Segment for Pain in KEGG and Metabolome Model. Supplemental information for Fig. 3b, which shows the zoomed segment for pain in the species and immune models. *Note, the reported biomarkers were calculated using the entire dataset and were not validated on held-out data. Abbreviations and Supporting Materials: Extended Data Fig. 5. Related to: Fig. 3.

Extended Data Fig. 5 Symptom-Specific Biomarkers - Different Correlation Patterns of Biomarkers to Symptom.

Supplemental information for Fig. 3c, which shows six pain biomarkers from multiple models. Here for each omics (a-d, Immune, Species, KEGG, Metabolome), we plotted the correlation of symptom-specific biomarkers (x-axis) to its related symptom (y-axis), colored by SHAP value (contribution to the symptom). P value by two-sided spearman correlation, FDR adjusted (Detailed statistics in Supplementary Table 5). Abbreviations: CD4, Cluster of Differentiation 4; CD8, Cluster of Differentiation 8; IFNg, Interferon Gamma; DC, Dendritic Cells; MAIT, Mucosal-Associated Invariant T; Th17, T helper 17 cells; CD4+ TCM, CD4+ Central Memory T cells; DC CD1c+ mBtp+, Dendritic Cells expressing CD1c+ and myelin basic protein; DC CD1c+ mHsp, Dendritic Cells expressing CD1c+ and heat shock protein; CD4+ TEM, CD4+ Effector Memory T cells; CD4+ Th17 rfx4+, CD4+ T helper 17 cells expressing RFX4; F. prausnitzii, Faecalibacterium prausnitzii; A. communis, Akkermansia communis; NAD, Nicotinamide Adenine Dinucleotide. Related to: Fig. 3.

Extended Data Fig. 6 omics WGCNA Modules and Host-Microbiome Network.

a, Correlation of WGCNA Modules with Clinical Metadata. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify co-expression modules for each omics layer: species, KEGG, immune, and metabolome. The top dendrograms show hierarchical clustering of ‘omics features, with modules identified. The bottom heatmap shows the relationship of module eigengenes (colored as per dendrogram) with clinical metadata – including demographic information and environmental factors - and 12 clinical scores. General linear models were used to determine the primary clinical drivers for each module, with the color gradient representing the coefficients (red = positive, blue = negative). Microbial modules were influenced by disease presence and energy-fatigue levels, while metabolome and immune modules correlated with age and gender. b-c, Microbiome-Immune-Metabolome Network in b, Patient and c, Healthy Subgroups. Supplemental information for Fig. 4a (Healthy Network) and 4b (Patient Subgroups). Figure 4a is the healthy network; here, Extended Data Fig. 6b presented the shifted correlations in all patients. Figure 4b represented the network in patient subgroups; here, Extended Data Fig. 6c is the corresponding healthy counterpart, for example, female patients were compared with female controls to exclude gender influences. d, Differences in Host-Microbiome Correlations between Healthy and Patient Subgroups. Selected host–microbiome module pairs are grouped on the x-axis (for example, pyruvate to blood modules, steroids to gut microbiome). Significant positive and negative correlations (top and bottom y-axis) of module members pairs are shown as dots for each subgroup (blue = healthy, orange = patient) (Spearman, adjusted p < 0.05), from left to right: Young, Elder, Female, Male, NormalWeight, OverWeight Healthy and Young, Elder, Female, Male, NormalWeight, OverWeight Patient. The middle bars represent the total count of associations. This panel highlights the shifts in host–microbiome networks from health to disease, for example, in patients, the loss of pyruvate to host blood modules correlation and the increase of INFg+ CD4 memory correlation with gut microbiome. Related to: Fig. 4.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2.

Reporting Summary

Supplementary Tables

Supplemental Tables 1–8.

Supplementary Software 1

BioMapAI model software with example data and tutorials. See full details in our Github: https://github.com/ohlab/BioMapAI.

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Xiong, R., Aiken, E., Caldwell, R. et al. AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome. Nat Med 31, 2991–3001 (2025). https://doi.org/10.1038/s41591-025-03788-3

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