Mobius: Mixture-Of-Experts Transformer Model in Epigenetics of ME/CFS and Long COVID
Bibliographic record
Abstract
Abstract Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID are chronic debilitating post-infectious illnesses that collectively affect up to 470 million individuals. Unlike illnesses of comparable scale, there are no validated blood or imaging tests for the clinical diagnosis of these conditions. Currently, these conditions are diagnosed through clinical exclusion, resulting in approximately 90% of ME/CFS patients being incorrectly diagnosed as Long COVID patients. This misdiagnosis contributes to delayed care and millions of dollars in healthcare burdens. We present Mobius, a transformer-based model that uses autoencoder-derived features from blood DNA methylation to distinguish ME/CFS, Long COVID, and healthy controls. Using 852 samples from 14 distinct datasets, our method employs three innovations: (i) self-supervised masked pretraining to learn epigenetic patterns, (ii) a sparsely-gated mixture-of-experts architecture to handle heterogeneous data, and (iii) an adaptive computation time mechanism for dynamic inference. Mobius achieved 97.06% accuracy (macro-F1 0.95, AUROC 0.96), outperforming current symptom-based diagnostics (58%) and baseline models such as XGBoost (82%). Ablation experiments showed that pretraining added 6% accuracy and that the gating and adaptive depth contributed an additional 7%. Our open-source pipeline could enable a much-needed objective blood test for these conditions and guide targeted precision medicine therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".