Quantification of uncertainty of peptide retention time predictions from a sequence-based model in LC-MS/MS proteomics experiments
Why this work is in the frame
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Bibliographic record
Abstract
In high-throughput mass spectrometry-based proteomics, it is necessary to employ separations to reduce sample complexity prior to mass spectrometric peptide identification. Interest has begun to focus on using information from separations to aid in peptide identification. One of the most common separations is reversed-phase liquid chromatography, in which peptides are separated on the basis of their chromatographic retention time. We apply a sequence-based model of peptide hydrophobicity to the problem of predicting peptide retention times, first fitting the model parameters using a large set of peptide identifications and then testing its predictions using a set of completely different peptide identifications. We demonstrate that not only does the model provide reasonably accurate predictions, it also provides a quantification of the uncertainty of its predictions. The model may therefore be used to provide checks on future tentative peptide identifications, even when the peptide species in question has never been observed before.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it