An Unsupervised Learning Method for Disease Classification Based on DNA Methylation Signatures
Bibliographic record
Abstract
Recent work has shown that genome-wide DNA methylation (DNAm) profiles can be used to discern signatures that can identify specific genetic disorders. These methods are especially effective at identifying single gene (Mendelian) disease, and methods to identify such signatures have been built by comparing methylation profiles of known disease versus control samples. These methods, however, have to-date been supervised, precluding the application of these methods to diseases with as-yet-unknown genetic cause. In this work, we tackle the problem of unsupervised disease classification based on DNAm signatures. Our method combines pre-filtration of the data to identify most promising methylation sites, clustering to identify co-varying sites, and an iterative method to further refine the signatures to build an effective clustering framework. We validate the proposed method on four diseases with known DNAm signatures (CHARGE, Kabuki, Sotos, and Weaver syndromes) and show high accuracy at determining the correct disease using unsupervised analysis. We also experiment with our approach on a novel dataset of patients with a clinical diagnosis of Autism, and illustrate the de novo identification of a specific subtype.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".