Lost in translation: five grand challenges for proteomic biomarker discovery
Bibliographic record
Abstract
The biomedical community has the imperative to develop reliable, clinically relevant and generalizable bioassays that can be used to accurately recognize those individuals with early-stage disease or those patients who will respond to therapy, with the ultimate aim of achieving individualized medicine. In recent years, increasingly sophisticated proteomic screening technologies have been introduced, providing the biomedical community with a valuable new approach for the systematic discovery and validation of novel diagnostic, prognostic and therapeutic tools. Nevertheless, the complexity of the cellular milieu wherein a variety of macromolecules interact in dynamic fashion, combined with the complex clinical manifestation of chronic pathologies and widespread diversity of patient populations, mean that universal biomarkers will not be easily developed. In this review, the five key challenges that must be surmounted in order to advance the clinical impact of this nascent field are described, and plausible solutions based on the authors' own ongoing proteomic profiling of cardiovascular disease is outlined.
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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 | 0.053 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".