Machine-learning-enhanced time-of-flight mass spectrometry analysis

Bibliographic Details
Title:	Machine-learning-enhanced time-of-flight mass spectrometry analysis
Authors:	Wei, Ye, Varanasi, Rama Srinivas, Schwarz, Torsten, Gomell, Leonie, Zhao, Huan, Larson, David J., Sun, Binhan, Liu, Geng, Chen, Hao, Raabe, Dierk, Gault, Baptiste
Publication Year:	2020
Collection:	Computer Science Condensed Matter
Subject Terms:	Condensed Matter - Materials Science, Computer Science - Machine Learning
More Details:	Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based from patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual user's expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry(ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. Comment: 20 pages, 15 figures
Document Type:	Working Paper
Access URL:	http://arxiv.org/abs/2010.01030
Accession Number:	edsarx.2010.01030
Database:	arXiv

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Items	– Name: Title Label: Title Group: Ti Data: Machine-learning-enhanced time-of-flight mass spectrometry analysis – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wei%2C+Ye%22">Wei, Ye</searchLink><br /><searchLink fieldCode="AR" term="%22Varanasi%2C+Rama+Srinivas%22">Varanasi, Rama Srinivas</searchLink><br /><searchLink fieldCode="AR" term="%22Schwarz%2C+Torsten%22">Schwarz, Torsten</searchLink><br /><searchLink fieldCode="AR" term="%22Gomell%2C+Leonie%22">Gomell, Leonie</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Huan%22">Zhao, Huan</searchLink><br /><searchLink fieldCode="AR" term="%22Larson%2C+David+J%2E%22">Larson, David J.</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Binhan%22">Sun, Binhan</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Geng%22">Liu, Geng</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Hao%22">Chen, Hao</searchLink><br /><searchLink fieldCode="AR" term="%22Raabe%2C+Dierk%22">Raabe, Dierk</searchLink><br /><searchLink fieldCode="AR" term="%22Gault%2C+Baptiste%22">Gault, Baptiste</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2020 – Name: Subset Label: Collection Group: HoldingsInfo Data: Computer Science<br />Condensed Matter – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Condensed+Matter+-+Materials+Science%22">Condensed Matter - Materials Science</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Science+-+Machine+Learning%22">Computer Science - Machine Learning</searchLink> – Name: Abstract Label: Description Group: Ab Data: Mass spectrometry is a widespread approach to work out what are the constituents of a material. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based from patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual user's expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry(ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis.<br />Comment: 20 pages, 15 figures – Name: TypeDocument Label: Document Type Group: TypDoc Data: Working Paper – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="http://arxiv.org/abs/2010.01030" linkWindow="_blank">http://arxiv.org/abs/2010.01030</link> – Name: AN Label: Accession Number Group: ID Data: edsarx.2010.01030
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RecordInfo	BibRecord: BibEntity: Subjects: – SubjectFull: Condensed Matter - Materials Science Type: general – SubjectFull: Computer Science - Machine Learning Type: general Titles: – TitleFull: Machine-learning-enhanced time-of-flight mass spectrometry analysis Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wei, Ye – PersonEntity: Name: NameFull: Varanasi, Rama Srinivas – PersonEntity: Name: NameFull: Schwarz, Torsten – PersonEntity: Name: NameFull: Gomell, Leonie – PersonEntity: Name: NameFull: Zhao, Huan – PersonEntity: Name: NameFull: Larson, David J. – PersonEntity: Name: NameFull: Sun, Binhan – PersonEntity: Name: NameFull: Liu, Geng – PersonEntity: Name: NameFull: Chen, Hao – PersonEntity: Name: NameFull: Raabe, Dierk – PersonEntity: Name: NameFull: Gault, Baptiste IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 10 Type: published Y: 2020
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