Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data

The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic methods in recent years. In this paper, machine le...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yu Wang, Mengru Sun, Yifan Duan
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/6ab98833eb2a459890a4774dc340d47b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:6ab98833eb2a459890a4774dc340d47b
record_format	dspace
spelling	oai:doaj.org-article:6ab98833eb2a459890a4774dc340d47b2021-11-22T01:09:53ZMetagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data1748-671810.1155/2021/8008731https://doaj.org/article/6ab98833eb2a459890a4774dc340d47b2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8008731https://doaj.org/toc/1748-6718The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic methods in recent years. In this paper, machine learning methods are used to analyze metagenomic sequencing data of acne, i.e., all kinds of lipids in the face skin. Firstly, lipids data of the diseased skin (DS) samples and the healthy skin (HS) samples of acne patients and the normal control (NC) samples of healthy person are, respectively, analyzed by using principal component analysis (PCA) and kernel principal component analysis (KPCA). Then, the lipids which have main influence on each kind of sample are obtained. In addition, a multiset canonical correlation analysis (MCCA) is utilized to get lipids which can differentiate the face skins of the above three samples. The experimental results show the machine learning methods can effectively analyze metagenomic sequencing data of acne. According to the results, lipids which only influence one of the three samples or the lipids which simultaneously have different degree of influence on these three samples can be used as indicators to judge skin statuses.Yu WangMengru SunYifan DuanHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Yu Wang Mengru Sun Yifan Duan Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data
description	The human health status can be assessed by the means of research and analysis of the human microbiome. Acne is a common skin disease whose morbidity increases year by year. The lipids which influence acne to a large extent are studied by metagenomic methods in recent years. In this paper, machine learning methods are used to analyze metagenomic sequencing data of acne, i.e., all kinds of lipids in the face skin. Firstly, lipids data of the diseased skin (DS) samples and the healthy skin (HS) samples of acne patients and the normal control (NC) samples of healthy person are, respectively, analyzed by using principal component analysis (PCA) and kernel principal component analysis (KPCA). Then, the lipids which have main influence on each kind of sample are obtained. In addition, a multiset canonical correlation analysis (MCCA) is utilized to get lipids which can differentiate the face skins of the above three samples. The experimental results show the machine learning methods can effectively analyze metagenomic sequencing data of acne. According to the results, lipids which only influence one of the three samples or the lipids which simultaneously have different degree of influence on these three samples can be used as indicators to judge skin statuses.
format	article
author	Yu Wang Mengru Sun Yifan Duan
author_facet	Yu Wang Mengru Sun Yifan Duan
author_sort	Yu Wang
title	Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data
title_short	Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data
title_full	Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data
title_fullStr	Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data
title_full_unstemmed	Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data
title_sort	metagenomic sequencing analysis for acne using machine learning methods adapted to single or multiple data
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/6ab98833eb2a459890a4774dc340d47b
work_keys_str_mv	AT yuwang metagenomicsequencinganalysisforacneusingmachinelearningmethodsadaptedtosingleormultipledata AT mengrusun metagenomicsequencinganalysisforacneusingmachinelearningmethodsadaptedtosingleormultipledata AT yifanduan metagenomicsequencinganalysisforacneusingmachinelearningmethodsadaptedtosingleormultipledata
_version_	1718418405825970176

Metagenomic Sequencing Analysis for Acne Using Machine Learning Methods Adapted to Single or Multiple Data

Ejemplares similares