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A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data
Fueled by technological advancement, there has been a surge of human microbiome studies surveying the microbial communities associated with the human body and their links with health and disease. As a complement to the human genome, the human microbiome holds great potential for precision medicine....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305753/ https://www.ncbi.nlm.nih.gov/pubmed/30619188 http://dx.doi.org/10.3389/fmicb.2018.03112 |
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author | Xiao, Jian Chen, Li Yu, Yue Zhang, Xianyang Chen, Jun |
author_facet | Xiao, Jian Chen, Li Yu, Yue Zhang, Xianyang Chen, Jun |
author_sort | Xiao, Jian |
collection | PubMed |
description | Fueled by technological advancement, there has been a surge of human microbiome studies surveying the microbial communities associated with the human body and their links with health and disease. As a complement to the human genome, the human microbiome holds great potential for precision medicine. Efficient predictive models based on microbiome data could be potentially used in various clinical applications such as disease diagnosis, patient stratification and drug response prediction. One important characteristic of the microbial community data is the phylogenetic tree that relates all the microbial taxa based on their evolutionary history. The phylogenetic tree is an informative prior for more efficient prediction since the microbial community changes are usually not randomly distributed on the tree but tend to occur in clades at varying phylogenetic depths (clustered signal). Although community-wide changes are possible for some conditions, it is also likely that the community changes are only associated with a small subset of “marker” taxa (sparse signal). Unfortunately, predictive models of microbial community data taking into account both the sparsity and the tree structure remain under-developed. In this paper, we propose a predictive framework to exploit sparse and clustered microbiome signals using a phylogeny-regularized sparse regression model. Our approach is motivated by evolutionary theory, where a natural correlation structure among microbial taxa exists according to the phylogenetic relationship. A novel phylogeny-based smoothness penalty is proposed to smooth the coefficients of the microbial taxa with respect to the phylogenetic tree. Using simulated and real datasets, we show that our method achieves better prediction performance than competing sparse regression methods for sparse and clustered microbiome signals. |
format | Online Article Text |
id | pubmed-6305753 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63057532019-01-07 A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data Xiao, Jian Chen, Li Yu, Yue Zhang, Xianyang Chen, Jun Front Microbiol Microbiology Fueled by technological advancement, there has been a surge of human microbiome studies surveying the microbial communities associated with the human body and their links with health and disease. As a complement to the human genome, the human microbiome holds great potential for precision medicine. Efficient predictive models based on microbiome data could be potentially used in various clinical applications such as disease diagnosis, patient stratification and drug response prediction. One important characteristic of the microbial community data is the phylogenetic tree that relates all the microbial taxa based on their evolutionary history. The phylogenetic tree is an informative prior for more efficient prediction since the microbial community changes are usually not randomly distributed on the tree but tend to occur in clades at varying phylogenetic depths (clustered signal). Although community-wide changes are possible for some conditions, it is also likely that the community changes are only associated with a small subset of “marker” taxa (sparse signal). Unfortunately, predictive models of microbial community data taking into account both the sparsity and the tree structure remain under-developed. In this paper, we propose a predictive framework to exploit sparse and clustered microbiome signals using a phylogeny-regularized sparse regression model. Our approach is motivated by evolutionary theory, where a natural correlation structure among microbial taxa exists according to the phylogenetic relationship. A novel phylogeny-based smoothness penalty is proposed to smooth the coefficients of the microbial taxa with respect to the phylogenetic tree. Using simulated and real datasets, we show that our method achieves better prediction performance than competing sparse regression methods for sparse and clustered microbiome signals. Frontiers Media S.A. 2018-12-19 /pmc/articles/PMC6305753/ /pubmed/30619188 http://dx.doi.org/10.3389/fmicb.2018.03112 Text en Copyright © 2018 Xiao, Chen, Yu, Zhang and Chen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Xiao, Jian Chen, Li Yu, Yue Zhang, Xianyang Chen, Jun A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data |
title | A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data |
title_full | A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data |
title_fullStr | A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data |
title_full_unstemmed | A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data |
title_short | A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data |
title_sort | phylogeny-regularized sparse regression model for predictive modeling of microbial community data |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305753/ https://www.ncbi.nlm.nih.gov/pubmed/30619188 http://dx.doi.org/10.3389/fmicb.2018.03112 |
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