[1]SUN Mengru,WANG Yu,HE Congfen,et al.Assisted analysis of acne metagenomic sequencing data using multi-set canonical correlation analysis methods[J].CAAI Transactions on Intelligent Systems,2020,15(5):972-977.[doi:10.11992/tis.201810005]
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Assisted analysis of acne metagenomic sequencing data using multi-set canonical correlation analysis methods

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 5 Page number: 972-977 Column: 学术论文—知识工程 Public date: 2020-09-05
Title:
Assisted analysis of acne metagenomic sequencing data using multi-set canonical correlation analysis methods
Author(s):
SUN Mengru1;  WANG Yu1;  HE Congfen2;  JIA Yan2;  GAO Xueyi1
1. Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China;
2. Key Laboratory of Cosmetic of China National Light Industry, School of Science, Beijing Technology and Business University, Beijing 100048, China
Keywords:
acne; macrogenomics; facial skin; lipids; machine learning; multi-set canonical correlation analysis
CLC:
TP391
DOI:
10.11992/tis.201810005
Abstract:
As one of the common skin diseases, the pathogenesis of acne is very complicated. The role of microbial colonization in the pathogenesis of acne is an active research area. The purpose of this paper is to analyze acne metagenomic data, including sample sets of acne diseased skin (DS) and healthy skin (HS) as well as normal control (NC) by using machine learning from the perspective of macrogenomics. Multi-set canonical correlation analysis (MCCA) method is used to analyze the above three sample sets at the same time and to confirm the lipids that can distinguish these three sample sets. The results show that lipids that had a significant impact on only one set and those that had different impacts on the three sample sets respectively can be used as indicators to determine the skin status. Moreover, these lipids can be used to guide diagnosis, prognosis, and treatment of skin acne diseases.
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