Peak selection
2 methods · 2011–2020
Peak selection: Deciding which peaks in a spectrum are real fragment ions before sequencing starts, by learned or evolved filters that remove noise and so shrink the search the sequencer faces.
Deciding which peaks in a spectrum are real fragment ions before sequencing starts, by learned or evolved filters that remove noise and so shrink the search the sequencer faces.
The earliest of its 2 methods is ProbPS (2011).
| Methods | 2 |
| Papers describing them | 3 |
| Authors | 9 |
| Active | 2011-08-17 to 2020-02-23 |
| Kinds | post-processor (2) |
| Acquisition | DDA (2) |
Methods (2)
Oldest first, by the paper that describes each one.
- ProbPS (2011): Peak selection model that quantifies how the presence of a derivative peak depends on its primary ion’s intensity, so noise can be discarded before sequencing. Benchmarked on the de novo and sequence-tag performance it enables rather than on peak counts.
- GP-based MS/MS spectrum denoising (2019): Genetic programming classifiers that separate b/y-ion peaks from noise before identification, raising PEAKS de novo identification rates on noisy CID spectra.
Papers describing them (3)
- ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity (2011, BMC Bioinformatics, peer-reviewed)
- Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification (2019, Journal of the American Society for Mass Spectrometry, peer-reviewed)
- A Decomposition Based Multi-objective Genetic Programming Algorithm for Classification of Highly Imbalanced Tandem Mass Spectrometry (2020, Lecture Notes in Computer Science, peer-reviewed)