ArticleJournal of proteome research2018
A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.
Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Article
- Alternate RNA decoding results in stable and abundant proteins in mammals.bioRxiv : the preprint server for biology · 2025Article
- Expression patterns and clinical value of key m6A RNA modification regulators in smoking patients with coronary artery disease.Epigenetics · 2024Article
- Bioinformatics Pipeline for Processing Single-Cell Data.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Identification and validation of a novel signature as a diagnostic and prognostic biomarker in colorectal cancer.Biology direct · 2022Article
- Diagnosis, clustering, and immune cell infiltration analysis of m6A-related genes in patients with acute myocardial infarction-a bioinformatics analysis.Journal of thoracic disease · 2022Article
- Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides.Frontiers in cell and developmental biology · 2021Article
- A cost-sensitive online learning method for peptide identification.BMC genomics · 2020Article
- Speeding Up Percolator.Journal of proteome research · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l
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Registered trials
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