CANDI: self-supervised, confidence-aware denoising imputation of genomic data
Bibliographic record
Abstract
Abstract Large-scale epigenomic datasets such as histone modifications and DNA accessibility have greatly advanced our understanding of genomic function. However, these measurements often suffer from noise, batch effects and irreproducibility. Epigenome imputation has emerged as a promising solution to these challenges. These methods integrate patterns across experiments, cell types, and genomic loci to predict the results of experiments, yielding predictions that often surpass observed data in quality. Thus, researchers increasingly leverage imputation for denoising data prior to downstream analysis. However, existing methods for imputation-based denoising have significant limitations. Here, we propose CANDI (Confidence-Aware Neural Denoising Imputer), a method for epigenome imputation that (1) predicts raw counts and handles experiment-specific covariates such as sequencing depth, (2) can (optionally) incorporate information from a low-quality existing experiment when predicting a target without retraining, and (3) outputs a calibrated measure of uncertainty. This approach is enabled using a Transformer model with self-supervised learning (SSL) training.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".