Mass Spectrometry–Based Proteomics: A Useful Tool for Biomarker Discovery?
Bibliographic record
Abstract
A biomarker is defined as a biological substance (i.e., protein, metabolite, specific post-translational modification) that can be used to detect a disease, measure its progression or the effects of a treatment. Importantly, a biomarker should be readily accessible (i.e., present within body fluids); it must also provide sufficient sensitivity and specificity to accurately distinguish between true positives, false positives, and false negatives. Even more importantly, detection of the biomarker should provide clinical benefits to the patient (i.e., improved survival and/or quality of life). Due to recent technical advances in biomolecular mass spectrometry, a great deal of effort has gone into the discovery of biomarkers at an international level. In this commentary we set forth our views on how mass spectrometry (MS) could be applied to the discovery of elusive biomarkers (Figure 1).
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".