The METLIN small molecule dataset for machine learning-based retention time prediction

The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.

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Autores principales:	Xavier Domingo-Almenara, Carlos Guijas, Elizabeth Billings, J. Rafael Montenegro-Burke, Winnie Uritboonthai, Aries E. Aisporna, Emily Chen, H. Paul Benton, Gary Siuzdak
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5d98ceacccd144fe87ea451c880b8095
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spelling	oai:doaj.org-article:5d98ceacccd144fe87ea451c880b80952021-12-02T13:27:31ZThe METLIN small molecule dataset for machine learning-based retention time prediction10.1038/s41467-019-13680-72041-1723https://doaj.org/article/5d98ceacccd144fe87ea451c880b80952019-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13680-7https://doaj.org/toc/2041-1723The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.Xavier Domingo-AlmenaraCarlos GuijasElizabeth BillingsJ. Rafael Montenegro-BurkeWinnie UritboonthaiAries E. AispornaEmily ChenH. Paul BentonGary SiuzdakNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xavier Domingo-Almenara Carlos Guijas Elizabeth Billings J. Rafael Montenegro-Burke Winnie Uritboonthai Aries E. Aisporna Emily Chen H. Paul Benton Gary Siuzdak The METLIN small molecule dataset for machine learning-based retention time prediction
description	The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.
format	article
author	Xavier Domingo-Almenara Carlos Guijas Elizabeth Billings J. Rafael Montenegro-Burke Winnie Uritboonthai Aries E. Aisporna Emily Chen H. Paul Benton Gary Siuzdak
author_facet	Xavier Domingo-Almenara Carlos Guijas Elizabeth Billings J. Rafael Montenegro-Burke Winnie Uritboonthai Aries E. Aisporna Emily Chen H. Paul Benton Gary Siuzdak
author_sort	Xavier Domingo-Almenara
title	The METLIN small molecule dataset for machine learning-based retention time prediction
title_short	The METLIN small molecule dataset for machine learning-based retention time prediction
title_full	The METLIN small molecule dataset for machine learning-based retention time prediction
title_fullStr	The METLIN small molecule dataset for machine learning-based retention time prediction
title_full_unstemmed	The METLIN small molecule dataset for machine learning-based retention time prediction
title_sort	metlin small molecule dataset for machine learning-based retention time prediction
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/5d98ceacccd144fe87ea451c880b8095
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The METLIN small molecule dataset for machine learning-based retention time prediction

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