Causality-driven candidate identification for reliable DNA methylation biomarker discovery
Bibliographic record
Abstract
Despite vast data support in DNA methylation (DNAm) biomarker discovery to facilitate health-care research, this field faces huge resource barriers due to preliminary unreliable candidates and the consequent compensations using expensive experiments. The underlying challenges lie in the confounding factors, especially measurement noise and individual characteristics. To achieve reliable identification of a candidate pool for DNAm biomarker discovery, we propose a Causality-driven Deep Regularization framework to reinforce correlations that are suggestive of causality with disease. It integrates causal thinking, deep learning, and biological priors to handle non-causal confounding factors, through a contrastive scheme and a spatial-relation regularization that reduces interferences from individual characteristics and noises, respectively. The comprehensive reliability of the proposed method was verified by simulations and applications involving various human diseases, sample origins, and sequencing technologies, highlighting its universal biomedical significance. Overall, this study offers a causal-deep-learning-based perspective with a compatible tool to identify reliable DNAm biomarker candidates, promoting resource-efficient biomarker discovery.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".