Data self-recalibration and mixture mass fingerprint searching (DASER-MMF) to enhance protein identification within complex mixtures
Bibliographic record
Abstract
A novel algorithm based on Data Self-Recalibration and a subsequent Mixture Mass Fingerprint search (DASER-MMF) has been developed to improve the performance of protein identification from online 1D and 2D-LC-MS/MS experiments conducted on high-resolution mass spectrometers. Recalibration of 40% to 75% of the MS spectra in a human serum dataset is demonstrated with average errors of 0.3 +/- 0.3 ppm, regardless of the original calibration quality. With simple protein mixtures, the MMF search identifies new proteins not found in the MS/MS based search and increases the sequence coverage for identified proteins by six times. The high mass accuracy allows proteins to be identified with as little as three peptide mass hits. When applied to very complex samples, the MMF search shows less dramatic performance improvements. However, refinements such as additional discriminating factors utilized within the search space provide significant gains in protein identification ability and indicate that further enhancements are possible in this realm.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".