Unifying protein inference and peptide identification with feedback to update consistency between peptides
Bibliographic record
Abstract
We first propose a new method to process peptide identification reports from databases search engines. Then via it we develop a method for unifying protein inference and peptide identification by adding a feedback from protein inference to peptide identification. The feedback information is a list of high-confidence proteins, which is used to update an adjacency matrix between peptides. The adjacency matrix is used in the regularization of peptide scores. Logistic regression (LR) is used to compute the probability of peptide identification with the regularized scores. Protein scores are then calculated with the LR probability of peptides. Instead of selecting the best peptide match for each MS/MS, we select multiple peptides. By testing on two datasets, the results have shown that the proposed method can robustly assign accurate probabilities to peptides, and have a higher discrimination power than PeptideProphet to distinguish correct and incorrect identified peptides. Additionally, not only can our method infer more true positive proteins but also infer less false positive proteins than ProteinProphet at the same false positive rate. The coverage of inferred proteins is also significantly increased due to the selection of multiple peptides for each MS/MS and the improvement of their scores by the feedback from the inferred proteins.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".