Parallelizing Peptide-Spectrum scoring using modern graphics processing units
Bibliographic record
Abstract
Tandem mass spectrometry is a powerful experimental tool used in molecular biology to determine the composition of protein mixtures. In a tandem mass experiment, peptide ion selection algorithms generally select only the most abundant peptide ions for further fragmentation. Because of this, the low-abundance proteins in a sample rarely get identified. A Real-Time Peptide-Spectrum Matching algorithm (RT-PSM) was introduced to achieve real-time peptide identification for solving this abundance related biases. Profiling results show that the Peptide-Spectrum similarity scoring is one of the most time-consuming module of RT-PSM. In this study, we develop a parallel algorithm for Peptide-Spectrum scoring using NVIDIA CUDA technology. As RT-PSM employs a scoring function based on shared peak counts, our algorithm can also be applied to other software that uses similar scoring schemes. Moreover, we introduce an algorithm to reduce the number of comparisons in calculating shared peak counts. In addition, as the CUDA architecture is unique, we introduce optimizations for the CUDA architecture to achieve better performance. A simulation shows a 190-fold speedup on the scoring module and a 26-fold speedup on the entire process. The developed algorithm can be employed to develop real-time control methods for tandem mass spectrometry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".