Quality Assessment of Tandem Mass Spectra by Using a Weighted K-Means
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Introduction The tandem mass spectrometer is a powerful tool with which to generate peptide (tandem) mass spectrum data for the analysis of complex biological protein mixtures in genomic-related disease cell lines. However, the majority of experimental tandem mass spectra cannot be interpreted by any database search engines. One of the main reasons this happens is that majority of experimental spectra are of quality too poor to be interpretable. Interpreting these “un-interpretable” spectra is a waste of time. Therefore, it is worthwhile to determine the quality of mass spectra before any interpretation. Objectives This paper proposes an approach to classifying tandem spectra into two groups: one with high quality and one with poor quality. Methods The proposed approach has two steps. First, each spectrum is mapped to a feature vector which describes the quality of the spectrum. Then, a weighted K-means clustering method is applied in order to classify the tandem mass spectra. Results and Conclusion Computational experiments illustrate that one cluster contains the majority of the high-quality spectra, while the other contains the majority of the poor-quality spectra. This result indicates that if we just search the spectra in the high-quality cluster, we can save the time for searching the majority of poor-quality spectra while losing a minimal amount of high-quality spectra. The software created for this work is available upon request.
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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 | 0.001 | 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 | 0.001 |
| 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 it