[1]孙梦茹,王瑜,何聪芬,等.基于MCCA的痤疮宏基因组数据辅助分析[J].智能系统学报,2020,15(5):972-977.[doi:10.11992/tis.201810005]
 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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基于MCCA的痤疮宏基因组数据辅助分析(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年5期
页码:
972-977
栏目:
学术论文—知识工程
出版日期:
2020-09-05

文章信息/Info

Title:
Assisted analysis of acne metagenomic sequencing data using multi-set canonical correlation analysis methods
作者:
孙梦茹1 王瑜1 何聪芬2 贾焱2 高学义1
1. 北京工商大学 计算机与信息工程学院 食品安全大数据技术北京市重点实验室,北京 100048;
2. 北京工商大学 理学院 中国轻工业化妆品重点实验室,北京 100048
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
关键词:
痤疮宏基因组学面部皮肤脂质机器学习多重集典型相关分析
Keywords:
acnemacrogenomicsfacial skinlipidsmachine learningmulti-set canonical correlation analysis
分类号:
TP391
DOI:
10.11992/tis.201810005
文献标志码:
A
摘要:
痤疮作为常见皮肤病之一,发病机制复杂,其中微生物定植在痤疮发病中的作用是一个热点研究问题。本文从宏基因组学的角度,利用机器学习方法分析痤疮宏基因组数据,包括痤疮患者的患病皮肤(diseased skin, DS)样本集和健康皮肤(healthy skin, HS)样本集,以及正常对照组(normal control, NC)样本集。为了同时分析3组样本集以获得可以区分不同样本集的脂质,使用多重集典型相关分析(multi-set canonical correlation analysis, MCCA)方法进行研究。实验结果可得到仅对某一样本集有显著影响的脂质,以及同时对3个样本集影响程度不同的脂质,这些脂质可以作为判别皮肤状态的指标,用于辅助指导皮肤痤疮疾病的诊断、预后和治疗。
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.

参考文献/References:

[1] MARONI G, ERMIDORO M, PREVIDI F, et al. Automated detection, extraction and counting of acne lesions for automatic evaluation and tracking of acne severity[C]//Proceedings of 2017 IEEE Symposium Series on Computational Intelligence. Honolulu, USA, 2017: 1-6.
[2] LUCUT S, SMITH M R. Dermatological tracking of chronic acne treatment effectiveness[C]//Proceedings of 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Orlando, USA, 2016: 5421-5426.
[3] THIBOUTOT D M, DRéNO B, ABANMI A, et al. Practical management of acne for clinicians: an international consensus from the global alliance to improve outcomes in acne[J]. Journal of the American academy of dermatology, 2018, 78(2, Suppl 1): S1-S23.e1.
[4] PAUGAM C, CORVEC S, SAINT-JEAN M, et al. Propionibacterium acnes phylotypes and acne severity: an observational prospective study[J]. Journal of the European academy of dermatology and venereology, 2017, 31(9): e398-e399.
[5] 王鸿. 寻常型痤疮发病机制研究进展[J]. 西南医科大学学报, 2018, 41(4): 385-388
WANG Hong. Research progress on the pathogenesis of acne vulgaris[J]. Journal of Southwest Medical University, 2018, 41(4): 385-388
[6] FITZ-GIBBON S, TOMIDA S, CHIU B H, et al. Propionibacterium acnes strain populations in the human skin microbiome associated with acne[J]. Journal of investigative dermatology, 2013, 133(9): 2152-2160.
[7] DAGNELIE M, CORVEC S, SAINT-JEAN M, et al. 461 Diversity of Propionibacterium acnes phylotypes according to body localization in acne patients versus healthy controls[J]. Journal of investigative dermatology, 2017, 137(10, Suppl 2): S271.
[8] ZOUBOULIS C C, JOURDAN E, PICARDO M. Acne is an inflammatory disease and alterations of sebum composition initiate acne lesions[J]. Journal of the European academy of dermatology and venereology, 2014, 28(5): 527-532.
[9] 吴贇, 吉杰, 张玲琳, 等. 微生物在痤疮发病中的作用[J]. 中国皮肤性病学杂志, 2016, 30(3): 311-314
WU Yun, JI Jie, ZHANG Linglin, et al. Roles of microorganisms in the pathogenesis of acne[J]. The Chinese journal of dermatovenereology, 2016, 30(3): 311-314
[10] ZHANG Xuegong, LIU Shansong, CUI Hongfei, et al. Reading the underlying information from massive metagenomic sequencing data[J]. Proceedings of the IEEE, 2017, 105(3): 459-473.
[11] VAN OPSTAL E J, BORDENSTEIN S R. Rethinking heritability of the microbiome[J]. Science, 2015, 349(6253): 1172-1173.
[12] KANG D W, PARK J G, ILHAN Z E, et al. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children[J]. PLoS one, 2013, 8(7): e68322.
[13] SEARS C L, GARRETT W S. Microbes, microbiota, and colon cancer[J]. Cell host & microbe, 2014, 15(3): 317-328.
[14] HSIAO E Y, MCBRIDE S W, HSIEN S, et al. Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders[J]. Cell, 2013, 155(7): 1451-1463.
[15] HUANG Shi, LI Rui, ZENG Xiaowei, et al. Predictive modeling of gingivitis severity and susceptibility via oral microbiota[J]. The ISME journal, 2014, 8(9): 1768-1780.
[16] WISITTIPANIT N, RANGWALA H, GILLEVET P, et al. SVM-based classification and feature selection methods for the analysis of Inflammatory Bowel disease microbiome data[C]//Proceedings of the 9th International Workshop on Data Mining in Bioinformatics. Washington, USA, 2010: 1-8.
[17] QIN Junjie, LI Yingrui, CAI Zhiming, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes[J]. Nature, 2012, 490(7418): 55-60.

备注/Memo

备注/Memo:
收稿日期:2019-10-09。
基金项目:国家自然科学基金面上项目(61671028);北京市自然科学基金面上项目(4162018)
作者简介:孙梦茹,硕士研究生,主要研究方向为图像处理、模式识别;王瑜,副教授,博士,主要研究方向为图像处理、模式识别。申请国家发明专利15项。主持国家自然科学基金面上项目2项、北京市自然科学基金面上项目、北京市"高创计划"青年拔尖人才资助项目,北京市高等学校青年拔尖人才培养计划项目等多个项目。出版学术专著2部,发表学术论文30余篇;何聪芬,教授,博士,主要研究方向为皮肤分子生态学与化妆品生物技术。主持纵向科研项目6项,作为主研人参加并完成国家自然科学基金项目1项。主持北京市教委纵向课题和参加973计划、863计划课题。合作主编著作2部,获批国家专利8项,国外专利1项。在美国国立生物技术信息中心(The National Center for Biotechnology Information (NCBI))注册新基因6个。参编著作2部。发表学术论文40余篇
通讯作者:王瑜.E-mail:wangyu@btbu.edu.cn
更新日期/Last Update: 2021-01-15