Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning-Reference-Cited by-同舟云学术

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Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning

Published:2024-09-09 Issue: Volume: Page:
ISSN:1044-0305
Container-title:Journal of the American Society for Mass Spectrometry
language:en
Short-container-title:J. Am. Soc. Mass Spectrom.

Author:

Kaskow Justin A.¹,Hahnert Eric T.¹,Porter Thomas K.¹^ORCID,Lu Yali²^ORCID,Stanev Valentin³,Niu Chendi²,Xu Wei²,Albarghouthi Methal²^ORCID,Wang Chunlei²^ORCID

Affiliation:

1. David H. Koch School of Chemical Engineering Practice, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

2. Analytical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland 20878, United States

3. Data Science and Modeling, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland 20878, United States

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/jasms.4c00137

Reference26 articles.

1. Quantitative proteomics: challenges and opportunities in basic and applied research

2. Quantitative mass spectrometry in proteomics: a critical review

3. Strategies for Drawing Quantitative Conclusions from Nontargeted Liquid Chromatography–High-Resolution Mass Spectrometry Analysis

4. Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

5. Absolute Quantification of Proteins by LCMSE

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