Analysis of redundant peaks in LC-MS/MS datasets
Bibliographic record
Abstract
LC-MS/MS is an analytical technique used for protein identification and biomarker discovery. Typically, each peptide will be measured twice by the MS/MS, resulting in two or more mass-to-charge spectra for each peptide. The current study investigates various ways to combine these replicate measurements to improve the quality of the measured spectra, and in turn, the confidence and accuracy of the protein identification. Sample data was collected using a QSTAR XL hybrid quadrupole-time-of-flight mass spectrometer, given a input standard protein mixture of known composition. Spectrum alignment is used to identify replicate spectra. Various algorithms for combining these replicate measurements are investigated, and are compared to the current industry standard MASCOT algorithm. Results are judged based on protein identification rates following combination of replicate spectra. Algorithms which leverage the fact that mass-to-charge data collected above the parent ion mass are more informative for protein identification appear particularly effective, and warrants further investigation.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".