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Published in 2022 at "Journal of proteome research"
DOI: 10.1021/acs.jproteome.2c00075
Abstract: Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching…
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Keywords:
spectral library;
percolator;
compomics spectral;
searching tool ... See more keywords
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Published in 2018 at "Journal of proteome research"
DOI: 10.1021/acs.jproteome.7b00767
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…
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Keywords:
large scale;
svm;
analysis;
percolator ... See more keywords
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Published in 2020 at "Journal of proteome research"
DOI: 10.1021/acs.jproteome.9b00780
Abstract: Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that…
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Keywords:
machine learning;
power;
scale experiments;
small scale ... See more keywords