Development and Evaluation of Methods for Predicting Protein Levels and Peptide Peak Intensities from Tandem Mass Spectrometry Data. Submitted for publication. Available at www.cs.toronto.edu/�bonner/papers.html
Bibliographic record
Abstract
Tandem mass spectrometry (MS/MS) of peptides is a central technology for proteomics, enabling the identification of thousands of proteins and peptides from a complex mixture. With the increasing acquisition rate of tandem mass spectrometers, it has become possible to use data-mining techniques to attempt to solve important biological problems using MS/MS data. These problems include (i) estimating the levels of the thousands of proteins in a tissue sample, (ii) predicting the intensity of the peaks in a mass spectrum, and (iii) explaining why different peptides from the same protein have different peak intensities. In this paper, we develop and evaluate several simple data-mining techniques for tackling these biological problems. The main data-mining problem is to untangle the various factors that affect the intensity of a peak in a mass spectrum. To this end, we develop three statistical models of MS/MS data: linear, exponentiated linear, and inverse linear. For each model, we develop a family of methods for fitting the model to the data. We test each method on both simulated and real-world data, where the real data consists of three datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse. Because of the highly skewed distribution of the data, which also ranges over several orders of magnitude, we measure the fit of each model to the data using Spearman’s rank correlation coefficient, instead of the more common Pearson correlation coefficient. Finally, we compare the methods to a number of naive methods, and report on their performance.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".