An adaptive approach to denoising tandem mass spectra
Bibliographic record
Abstract
In our recently developed denoising method, a linear combination of five features is used to adjust the peak intensities in tandem mass spectra. Although the method shows a promise, the coefficients (weights) of the linear combination were fixed and determined empirically. In this paper, we propose an adaptive approach for estimating these weights. The proposed approach: (1) calculates the score for each peak in a data set with the empirically determined weights in, (2) selects the training dataset based on the scores of peaks, (3) applies the LDA (Linear discriminant analysis) to the training dataset and take the solution of LDA as the new weights, (4) calculates the score again with new weights, (5) repeats (2) - (4) until weights have no significant change. After getting the final weights, the proposed approach follows the methods developed in. The proposed approach is applied to two tandem mass spectra datasets: ISB (with low resolution) and TOV-Q (with high resolution) to evaluate its performance. The results show that about 66% of peaks (likely noise peaks) can be removed and that the number of peptides identified by Mascot increases by 14% and 21% for ISB and TOV-Q dataset, respectively, comparing to the previous work.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".