MRM-based Protein Quantification with Labeled Standards for Biomarker Discovery, Verification, and Validation in Human Plasma
Bibliographic record
Abstract
Multiple reaction monitoring (also called selected reaction monitoring) is a targeted technique and has been proposed and used for the verification of biomarkers, which have been “discovered” by means of a different technique. This biomarker discovery step has usually been based on some type of differential expression analysis—either mass spectrometry-based or an alternative technique, such as 2-D gels—that produces results in terms of “fold changes”. MRM analysis, which can provide results in terms of protein concentration, holds great promise for the high-throughput verification and validation of candidate biomarkers in human biofluids, such as blood plasma. In addition, because MRM assays are able to include increasingly complex panels of proteins in a single assay (multiplexing), they can also be used as biomarker discovery tools, enabling the simultaneous screening of large numbers of proteins for a variety of diseases, including non-communicable diseases, such as cardiovascular disease and cancer. This enables the discovery of biomarker panels, comprised of several proteins, which often have higher diagnostic accuracies than can be obtained through the use of single proteins as biomarkers. Based on screening results, MRM-based assays for smaller sets of potential biomarkers can then be developed in order to validate these biomarker panels on large numbers of patient samples.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".