Quantitative analysis with modern bioanalytical mass spectrometry and stable isotope labeling
Bibliographic record
Abstract
Abstract The invention of new ionization techniques namely electrospray ionization and matrix‐assisted laser desorption/ionization combined with the development of novel mass spectrometer analyzers and evolving isotope‐ratio mass spectrometry have fueled the presence and use of modern mass spectrometric methodologies in many bioanalytical laboratories. Consequently, over the past two decades, a steadily increasing number of quantitative methods employing stable isotope labeling techniques have been reported, including prominent examples of methods to determine differential expression of proteins in disease studies, new‐born screening for metabolic disorders, and tracing drugs or dietary compounds and their respective metabolites. Labeling biomolecules for quantitative studies using mass spectrometry has several challenges, including potentially insufficient labeling efficiency, ionization suppression, chromatographic separation of labeled and non‐labeled compounds, and isotope exchange with the environment. It is not surprising that method development to minimize or eliminate existing limitations represents a very active and dynamic research area. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".