Reducing Peptide Sequence Bias in Quantitative Mass Spectrometry Data with Machine Learning
Author:
Affiliation:
1. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
2. Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
Funder
National Institute of General Medical Sciences
Publisher
American Chemical Society (ACS)
Subject
General Chemistry,Biochemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.2c00211
Reference15 articles.
1. Computational prediction of proteotypic peptides for quantitative proteomics
2. Prediction of peptides observable by mass spectrometry applied at the experimental set level
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4. Prediction of high-responding peptides for targeted protein assays by mass spectrometry
5. CONSeQuence: Prediction of Reference Peptides for Absolute Quantitative Proteomics Using Consensus Machine Learning Approaches
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