The Role of Mass Spectrometry in Biomarker Discovery and Measurement
Bibliographic record
Abstract
Recent advances in the biological and analytical sciences have led to unprecedented interest in the discoveryand quantitation of endogenous molecules that serve as indicators of drug safety, mechanism of action, efficacy, and dis-ease state progression. By allowing for improved decision-making, these indicators, referred to as biomarkers, can dra-matically improve the efficiency of drug discovery and development. Mass spectrometry has been a key part of biomarkerdiscovery and evaluation owing to several important attributes, which include sensitive and selective detection, multi-analyte analysis, and the ability to provide structural information. Because of these capabilities, mass spectrometry hasbeen widely deployed in search for new markers both through the analysis of large molecules (proteomics) and smallmolecules (metabonomics). In addition, mass spectrometry is increasingly being used to support quantitative measurementto assist in the evaluation and validation of biomarker leads. In this revi ew, the dual role of mass spectrometry for bio-marker discovery and measurement is explored for both large and small molecules by examining the key technologies andmethods used along the continuum from drug discovery through clinical development. Keywords: Biomarker discovery, biomarker quantitation, proteomics, metabonomics, liquid chromatography massspectrometry (LC/MS), gas chromatography mass spectrometry (GC/MS), stable isotope internal standard, matrix assisted laserdesorption ionization, electrospray ionization
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 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".