MetImputBERT: a pretrained BERT framework for missing value imputation in NMR metabolomics data
Bibliographic record
Abstract
Missing values in nuclear magnetic resonance metabolomics data compromise downstream clinical interpretation. Here, we present MetImputBERT, an imputation method based on a pretrained BERT framework. MetImputBERT uses the masks in the masked language model to simulate missing values and leverages predictions and reconstructions to these positions to simulate the imputation process. The learning of MetImputBERT is driven by minimizing the reconstruction error. MetImputBERT was pretrained on the largest metabolomics dataset to date, comprising data from over 230 000 individuals in the UK Biobank. When new datasets with missing values were encountered, MetImputBERT loaded the pretrained parameters and directly imputed the missing values by inferring their reconstructed estimates. MetImputBERT outperformed commonly used methods-K-nearest neighbors, multiple imputation by chained equations, and singular value decomposition-in imputation performance on two independent test sets. We provide an open-source Python tool that allows users to quickly impute missing values in their own NMR metabolomics data without any additional 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".