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Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data. / Melnikov, Arsenty D.; Tsentalovich, Yuri P.; Yanshole, Vadim V.

In: Analytical Chemistry, Vol. 92, No. 1, 07.01.2020, p. 588-592.

Research output: Contribution to journal › Article › peer-review

Harvard

Melnikov, AD, Tsentalovich, YP & Yanshole, VV 2020, 'Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data', Analytical Chemistry, vol. 92, no. 1, pp. 588-592. https://doi.org/10.1021/acs.analchem.9b04811

APA

Melnikov, A. D., Tsentalovich, Y. P., & Yanshole, V. V. (2020). Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data. Analytical Chemistry, 92(1), 588-592. https://doi.org/10.1021/acs.analchem.9b04811

Vancouver

Melnikov AD, Tsentalovich YP, Yanshole VV. Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data. Analytical Chemistry. 2020 Jan 7;92(1):588-592. doi: 10.1021/acs.analchem.9b04811

Author

Melnikov, Arsenty D. ; Tsentalovich, Yuri P. ; Yanshole, Vadim V. / Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data. In: Analytical Chemistry. 2020 ; Vol. 92, No. 1. pp. 588-592.

BibTeX

@article{db3c476dcf684b4ba88889332fd729cb,
title = "Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data",
abstract = "This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography-mass spectrometry (LC-MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC-MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC- or LC-MS techniques. Peakonly is freely available on GitHub (https://github.com/arseha/peakonly) under an MIT license.",
author = "Melnikov, {Arsenty D.} and Tsentalovich, {Yuri P.} and Yanshole, {Vadim V.}",
year = "2020",
month = jan,
day = "7",
doi = "10.1021/acs.analchem.9b04811",
language = "English",
volume = "92",
pages = "588--592",
journal = "Analytical Chemistry",
issn = "0003-2700",
publisher = "American Chemical Society",
number = "1",

}

RIS

TY - JOUR

T1 - Deep Learning for the Precise Peak Detection in High-Resolution LC-MS Data

AU - Melnikov, Arsenty D.

AU - Tsentalovich, Yuri P.

AU - Yanshole, Vadim V.

PY - 2020/1/7

Y1 - 2020/1/7

N2 - This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography-mass spectrometry (LC-MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC-MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC- or LC-MS techniques. Peakonly is freely available on GitHub (https://github.com/arseha/peakonly) under an MIT license.

AB - This letter is devoted to the application of machine learning, namely, convolutional neural networks to solve problems in the initial steps of the common pipeline for data analysis in metabolomics. These steps are the peak detection and the peak integration in raw liquid chromatography-mass spectrometry (LC-MS) data. Widely used algorithms suffer from rather poor precision for these tasks, yielding many false positive signals. In the present work, we developed an algorithm named peakonly, which has high flexibility for the detection or exclusion of low-intensity noisy peaks, and shows excellent quality in the detection of true positive peaks, approaching the highest possible precision. The current approach was developed for the analysis of high-resolution LC-MS data for the purposes of metabolomics, but potentially it can be applied with several adaptations in other fields, which utilize high-resolution GC- or LC-MS techniques. Peakonly is freely available on GitHub (https://github.com/arseha/peakonly) under an MIT license.

UR - http://www.scopus.com/inward/record.url?scp=85077468725&partnerID=8YFLogxK

U2 - 10.1021/acs.analchem.9b04811

DO - 10.1021/acs.analchem.9b04811

M3 - Article

C2 - 31841624

AN - SCOPUS:85077468725

VL - 92

SP - 588

EP - 592

JO - Analytical Chemistry

JF - Analytical Chemistry

SN - 0003-2700

IS - 1

ER -

ID: 23001016