Determining the Overall Merit of Protein Identification Data Sets: <i>rho-</i>Diagrams and<i>rho-</i>Scores
Bibliographic record
Abstract
This paper described a simple heuristic method for determining the merit of a set of peptide sequence assignments made using tandem mass spectra. The method involved comparing a prediction based on the known stochastic behavior of a sequence assignment algorithm with the assignments generated from a particular data set. A particular formulation of this comparison was defined through the construction of a plot of the data, the rho-diagram, as well as a parameter derived from this plot, the rho-score. This plot and parameter were shown to be able to readily characterize the relative quality of a set of peptide sequence assignments and to allow the straightforward determination of probability threshold values for the interpretation of proteomics data. This plot is independent of the algorithm or scoring scheme used to estimate the statistical significance of a set of experimental results; rather, it can be used as an objective test of the correctness of those estimates. The rho-score can also be used as a parameter to evaluate the relative merit of protein identifications, such as those made across proteome species taxonomic categories.
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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 | 0.005 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".