Extracting a COVID-19 signature from a multi-omic dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: The complexity of COVID-19 requires approaches that extend beyond symptom-based descriptors. Multi-omic data, combining clinical, proteomic, and metabolomic information, offer a more detailed view of disease mechanisms and biomarker discovery. Methods: As part of a large-scale Quebec initiative, we collected extensive datasets from COVID-19 positive and negative patient samples. Using a multi-view machine learning framework with ensemble methods, we integrated thousands of features across clinical, proteomic, and metabolomic domains to classify COVID-19 status. We further applied a novel feature relevance methodology to identify condensed signatures. Results: Our models achieved a balanced accuracy of 89% ± 5% despite the high-dimensional nature of the data. Feature selection yielded 12- and 50-feature signatures that improved classification accuracy by at least 3% compared to the full feature set. These signatures were both accurate and interpretable. Discussion: This work demonstrates that multi-omic integration, combined with advanced machine learning, enables the extraction of robust COVID-19 signatures from complex datasets. The condensed biomarker sets provide a practical path toward improved diagnosis and precision medicine, representing a significant advancement in COVID-19 biomarker discovery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it