ProPickML: Advancing Clinical Diagnostics with Automated Peak Picking in Label-Free Targeted Proteomics
Bibliographic record
Abstract
In targeted proteomics utilizing Selected Reaction Monitoring (SRM), the precise detection of specific peptides within complex mixtures remains a significant challenge, particularly due to noise and interference in chromatograms. Existing methodologies, such as isotopic labeling and scoring algorithms, offer partial solutions but are constrained by high run times and elevated false discovery rates. To address these limitations, we have developed ProPickML a machine learning-based tool designed to accurately identify peptide peaks across diverse data sets, independent of the assumed presence of the peptide. This model was trained on a manually labeled data set and subsequently validated to assess its predictive accuracy. The results demonstrate that the model reliably identifies peptide peaks in the presence of noise, achieving a Matthews correlation coefficient (MCC) of 0.81 on an independent test data set, surpassing mProphet's MCC of 0.71. Implemented in R as ProPickML, this tool offers a competitive, cost-effective alternative to existing techniques, significantly reducing reliance on isotopic labeling and enhancing the accuracy of peptide identification in SRM workflows.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".