Peptide-Centric Approaches Provide an Alternative Perspective To Re-Examine Quantitative Proteomic Data
Bibliographic record
Abstract
Quantitative proteomics can provide rich information on changes in biological functions and processes. However, its accuracy is affected by the inherent information degeneration found in bottom-up proteomics. Therefore, the precise protein inference from identified peptides can be mistaken since an ad hoc rule is used for generating a list of protein groups that depends on both the sample type and the sampling depth. Herein, we propose an alternative approach for examining quantitative proteomic data which is peptide-centric instead of protein-centric. We discuss the feasibility of the peptide-centric approach which was tested on several quantitative proteomic data sets. We show that peptide-centric quantification has several advantages over protein level analysis: (1) it is more sensitive for sample segregation, (2) it avoids the issues associated with protein inference, and (3) it can retrieve significant peptides lost in protein-centric quantification for further downstream analysis.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".