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Xu LL, Young A, Zhou A, et al. Machine Learning in Mass Spectrometric Analysis of DIA Data. Proteomics. 2020;20(21):e1900352doi: 10.1002/pmic.201900352.
Xu, L. L., Young, A., Zhou, A., & Röst, H. L. (2020). Machine Learning in Mass Spectrometric Analysis of DIA Data. Proteomics, 20(21), e1900352. https://doi.org/10.1002/pmic.201900352
Xu, Leon L, et al. "Machine Learning in Mass Spectrometric Analysis of DIA Data." Proteomics vol. 20,21 (2020): e1900352. doi: https://doi.org/10.1002/pmic.201900352
Xu LL, Young A, Zhou A, Röst HL. Machine Learning in Mass Spectrometric Analysis of DIA Data. Proteomics. 2020 Nov;20(21):e1900352. doi: 10.1002/pmic.201900352. Epub 2020 Mar 04. PMID: 32061181.
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Proteomics. 2020 Nov;20(21):e1900352. doi: 10.1002/pmic.201900352. Epub 2020 Mar 04.

Machine Learning in Mass Spectrometric Analysis of DIA Data.

Proteomics

Leon L Xu, Adamo Young, Audrina Zhou, Hannes L Röst

Affiliations

  1. The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Room 230, Toronto, Ontario, M5S 3E1, Canada.

PMID: 32061181 DOI: 10.1002/pmic.201900352

Abstract

Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS) based methods are currently the top choice for high-throughput, quantitative measurements of the proteome. While traditional proteomics LC-MS/MS methods can suffer from issues such as low reproducibility and quantitative accuracy due to its stochastic nature, recent improvements in acquisition protocols have resulted in methods that can overcome these challenges. Data-independent acquisition (DIA) is a novel mass spectrometric method that does so by using a deterministic acquisition strategy. These new approaches will allow researchers to apply MS on more complex samples, however, existing heuristic and expert-knowledge based methods will struggle with keeping pace of the increasing complexity of the resulting data. Deep learning (DL) based methods have been shown to be more adept at handling large amounts of complex data than traditional methods in many other fields, such as computer vision and natural language processing. Proteomics is also entering a phase where the size and complexity of the data will require us to look towards scalable and data-driven DL pipelines.

© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords: data independent acquisition; deep learning; machine learning; proteomics

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