Clinical translation of MS-based, quantitative plasma proteomics: status, challenges, requirements, and potential
Bibliographic record
Abstract
INTRODUCTION: Aided by the advent of advanced mass spectrometry (MS)-based technologies and methodologies, quantitative proteomics has emerged as a viable technique to capture meaningful data for candidate biomarker evaluation. To aid clinical translation, these methods generally utilize a bottom-up strategy with isotopically labeled standards and a targeted form of MS measurement. AREAS COVERED: This article reviews the status, challenges, requirements, and potential of translating current, MS-based methods to the clinical laboratory. The described methods are discussed and contrasted within a fit-for-purpose approach, while different resources for quality control, quantitative analysis, and data interpretation are additionally provided. Expert commentary: Although great strides have been made over the past five years in developing reliable quantitative assays for plasma protein biomarkers, it is crucial for investigators to have an understanding of the clinical validation process, a major roadblock in translational research. Continued progress in method design and validation of protein assays is necessary to ultimately achieve widespread adoption and regulatory approval.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".