Protein Identification and Peptide Expression Resolver: Harmonizing Protein Identification with Protein Expression Data
Bibliographic record
Abstract
Proteomic discovery platforms generate both peptide expression information and protein identification information. Peptide expression data are used to determine which peptides are differentially expressed between study cohorts, and then these peptides are targeted for protein identification. In this paper, we demonstrate that peptide expression information is also a powerful tool for enhancing confidence in protein identification results. Specifically, we evaluate the following hypothesis: tryptic peptides originating from the same protein have similar expression profiles across samples in the discovery study. Evidence supporting this hypothesis is provided. This hypothesis is integrated into a protein identification tool, PIPER (Protein Identification and Peptide Expression Resolver), that reduces erroneous protein identifications below 5%. PIPER's utility is illustrated by application to a 72-sample biomarker discovery study where it is demonstrated that false positive protein identifications can be reduced below 5%. Consequently, it is recommended that PIPER methodology be incorporated into proteomic studies where both protein expression and identification data are collected.
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 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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".