Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.

Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we intr...

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Autores principales:	Shoaib Bin Masud, Conor Jenkins, Erika Hussey, Seth Elkin-Frankston, Phillip Mach, Elizabeth Dhummakupt, Shuchin Aeron
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ff97c7d7b98d40f8b88205d0084b8322
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spelling	oai:doaj.org-article:ff97c7d7b98d40f8b88205d0084b83222021-12-02T20:08:56ZUtilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.1932-620310.1371/journal.pone.0255240https://doaj.org/article/ff97c7d7b98d40f8b88205d0084b83222021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255240https://doaj.org/toc/1932-6203Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers' attention for further analysis.Shoaib Bin MasudConor JenkinsErika HusseySeth Elkin-FrankstonPhillip MachElizabeth DhummakuptShuchin AeronPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255240 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shoaib Bin Masud Conor Jenkins Erika Hussey Seth Elkin-Frankston Phillip Mach Elizabeth Dhummakupt Shuchin Aeron Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.
description	Metabolomic data processing pipelines have been improving in recent years, allowing for greater feature extraction and identification. Lately, machine learning and robust statistical techniques to control false discoveries are being incorporated into metabolomic data analysis. In this paper, we introduce one such recently developed technique called aggregate knockoff filtering to untargeted metabolomic analysis. When applied to a publicly available dataset, aggregate knockoff filtering combined with typical p-value filtering improves the number of significantly changing metabolites by 25% when compared to conventional untargeted metabolomic data processing. By using this method, features that would normally not be extracted under standard processing would be brought to researchers' attention for further analysis.
format	article
author	Shoaib Bin Masud Conor Jenkins Erika Hussey Seth Elkin-Frankston Phillip Mach Elizabeth Dhummakupt Shuchin Aeron
author_facet	Shoaib Bin Masud Conor Jenkins Erika Hussey Seth Elkin-Frankston Phillip Mach Elizabeth Dhummakupt Shuchin Aeron
author_sort	Shoaib Bin Masud
title	Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.
title_short	Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.
title_full	Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.
title_fullStr	Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.
title_full_unstemmed	Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.
title_sort	utilizing machine learning with knockoff filtering to extract significant metabolites in crohn's disease with a publicly available untargeted metabolomics dataset.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/ff97c7d7b98d40f8b88205d0084b8322
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Utilizing machine learning with knockoff filtering to extract significant metabolites in Crohn's disease with a publicly available untargeted metabolomics dataset.

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