基于马尔可夫随机场模型的混合图形模型识别疾病相关多组学网络。

IF 1.7 4区 医学 Q3 GENETICS & HEREDITY
Jaehyun Park, Sungho Won
{"title":"基于马尔可夫随机场模型的混合图形模型识别疾病相关多组学网络。","authors":"Jaehyun Park,&nbsp;Sungho Won","doi":"10.1002/gepi.22605","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.550 vs. 0.730). Furthermore, FMGM was applied to identify multiomics profiles associated with AD, and significance association was found for the correlation between carotenoid biosynthesis and RNA degradation, suggesting the importance of metabolism related to oxidative stress and microbial RNA balance. R codes can be accessed as an R package “fusedMGM,” and an example data set and a script for analyses can be found at http://figshare.com/articles/dataset/FMGM_synthetic_data_example_zip/20509113.</p>\n </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"49 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying Disease Associated Multi-Omics Network With Mixed Graphical Models Based on Markov Random Field Model\",\"authors\":\"Jaehyun Park,&nbsp;Sungho Won\",\"doi\":\"10.1002/gepi.22605\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.550 vs. 0.730). Furthermore, FMGM was applied to identify multiomics profiles associated with AD, and significance association was found for the correlation between carotenoid biosynthesis and RNA degradation, suggesting the importance of metabolism related to oxidative stress and microbial RNA balance. R codes can be accessed as an R package “fusedMGM,” and an example data set and a script for analyses can be found at http://figshare.com/articles/dataset/FMGM_synthetic_data_example_zip/20509113.</p>\\n </div>\",\"PeriodicalId\":12710,\"journal\":{\"name\":\"Genetic Epidemiology\",\"volume\":\"49 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-01-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genetic Epidemiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gepi.22605\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genetic Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gepi.22605","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0

摘要

在本文中,我们提出了一种新的方法,称为融合混合图形模型(FMGM),可以推断与二分类表型相关的网络结构。FMGM基于两两马尔可夫随机场模型,并进行统计分析,从6月龄婴儿的多组学数据中寻找特应性皮炎(AD)的生物标志物和潜在网络结构。利用幂律网络的合成数据集对FMGM的性能进行了模拟评估,结果表明,FMGM在识别网络差异方面的性能优于使用先前方法causalMGM的单独推理(f1分数为0.550比0.730)。此外,FMGM应用于识别AD相关的多组学图谱,发现类胡萝卜素生物合成与RNA降解之间存在显著相关性,提示氧化应激和微生物RNA平衡相关代谢的重要性。R代码可以作为R包“fusedMGM”访问,并且可以在http://figshare.com/articles/dataset/FMGM_synthetic_data_example_zip/20509113找到示例数据集和用于分析的脚本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Disease Associated Multi-Omics Network With Mixed Graphical Models Based on Markov Random Field Model

In this article, we proposed a new method named fused mixed graphical model (FMGM), which can infer network structures associated with dichotomous phenotypes. FMGM is based on a pairwise Markov random field model, and statistical analyses including the proposed method were conducted to find biological markers and underlying network structures of the atopic dermatitis (AD) from multiomics data of 6-month-old infants. The performance of FMGM was evaluated with simulations by using synthetic datasets of power-law networks, showing that FMGM had superior performance for identifying the differences of the networks compared to the separate inference with the previous method, causalMGM (F1-scores 0.550 vs. 0.730). Furthermore, FMGM was applied to identify multiomics profiles associated with AD, and significance association was found for the correlation between carotenoid biosynthesis and RNA degradation, suggesting the importance of metabolism related to oxidative stress and microbial RNA balance. R codes can be accessed as an R package “fusedMGM,” and an example data set and a script for analyses can be found at http://figshare.com/articles/dataset/FMGM_synthetic_data_example_zip/20509113.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Genetic Epidemiology
Genetic Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
4.40
自引率
9.50%
发文量
49
审稿时长
6-12 weeks
期刊介绍: Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed on the relative contribution of genetic and environmental factors to human disease as revealed by genetic, epidemiological, and biologic investigations. Genetic Epidemiology primarily publishes papers in statistical genetics, a research field that is primarily concerned with development of statistical, bioinformatical, and computational models for analyzing genetic data. Incorporation of underlying biology and population genetics into conceptual models is favored. The Journal seeks original articles comprising either applied research or innovative statistical, mathematical, computational, or genomic methodologies that advance studies in genetic epidemiology. Other types of reports are encouraged, such as letters to the editor, topic reviews, and perspectives from other fields of research that will likely enrich the field of genetic epidemiology.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信