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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 | 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".