Permutation-based identification of important biomarkers for complex diseases via machine learning models

Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Here, the authors propose a computationally efficient Permutation-based Feature Importance Test to assist interpretation and selection of indivi...

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Autores principales:	Xinlei Mi, Baiming Zou, Fei Zou, Jianhua Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a14b8c9fa4db459da160a337cbf281f4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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spelling	oai:doaj.org-article:a14b8c9fa4db459da160a337cbf281f42021-12-02T15:45:24ZPermutation-based identification of important biomarkers for complex diseases via machine learning models10.1038/s41467-021-22756-22041-1723https://doaj.org/article/a14b8c9fa4db459da160a337cbf281f42021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22756-2https://doaj.org/toc/2041-1723Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Here, the authors propose a computationally efficient Permutation-based Feature Importance Test to assist interpretation and selection of individual features in complex machine learning models for complex disease analysis.Xinlei MiBaiming ZouFei ZouJianhua HuNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Xinlei Mi Baiming Zou Fei Zou Jianhua Hu Permutation-based identification of important biomarkers for complex diseases via machine learning models
description	Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Here, the authors propose a computationally efficient Permutation-based Feature Importance Test to assist interpretation and selection of individual features in complex machine learning models for complex disease analysis.
format	article
author	Xinlei Mi Baiming Zou Fei Zou Jianhua Hu
author_facet	Xinlei Mi Baiming Zou Fei Zou Jianhua Hu
author_sort	Xinlei Mi
title	Permutation-based identification of important biomarkers for complex diseases via machine learning models
title_short	Permutation-based identification of important biomarkers for complex diseases via machine learning models
title_full	Permutation-based identification of important biomarkers for complex diseases via machine learning models
title_fullStr	Permutation-based identification of important biomarkers for complex diseases via machine learning models
title_full_unstemmed	Permutation-based identification of important biomarkers for complex diseases via machine learning models
title_sort	permutation-based identification of important biomarkers for complex diseases via machine learning models
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a14b8c9fa4db459da160a337cbf281f4
work_keys_str_mv	AT xinleimi permutationbasedidentificationofimportantbiomarkersforcomplexdiseasesviamachinelearningmodels AT baimingzou permutationbasedidentificationofimportantbiomarkersforcomplexdiseasesviamachinelearningmodels AT feizou permutationbasedidentificationofimportantbiomarkersforcomplexdiseasesviamachinelearningmodels AT jianhuahu permutationbasedidentificationofimportantbiomarkersforcomplexdiseasesviamachinelearningmodels
_version_	1718385768226881536

Permutation-based identification of important biomarkers for complex diseases via machine learning models

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