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DOI | 10.1186/1752-0509-7-119 |
Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes | |
Williams-DeVane, ClarLynda R.1; Reif, David M.2; Hubal, Elaine Cohen2; Bushel, Pierre R.3; Hudgens, Edward E.4; Gallagher, Jane E.4; Edwards, Stephen W.1 | |
发表日期 | 2013-11-04 |
ISSN | 1752-0509 |
卷号 | 7 |
英文摘要 | Background: Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results: A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student's t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions: The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. |
英文关键词 | Asthma;Endotypes;Gene Expression;Integrated analysis |
语种 | 英语 |
WOS记录号 | WOS:000327504500001 |
来源期刊 | BMC SYSTEMS BIOLOGY
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来源机构 | 美国环保署 |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/59513 |
作者单位 | 1.US EPA, Natl Hlth & Environm Effects Res Lab, Integrated Syst Toxicol Div, Durham, NC 27711 USA; 2.US EPA, Natl Ctr Computat Toxicol, Durham, NC 27711 USA; 3.NIEHS, Biostat Branch, Durham, NC 27709 USA; 4.US EPA, Natl Hlth & Environm Effects Res Lab, Environm Publ Hlth Div, Durham, NC 27711 USA |
推荐引用方式 GB/T 7714 | Williams-DeVane, ClarLynda R.,Reif, David M.,Hubal, Elaine Cohen,et al. Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes[J]. 美国环保署,2013,7. |
APA | Williams-DeVane, ClarLynda R..,Reif, David M..,Hubal, Elaine Cohen.,Bushel, Pierre R..,Hudgens, Edward E..,...&Edwards, Stephen W..(2013).Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes.BMC SYSTEMS BIOLOGY,7. |
MLA | Williams-DeVane, ClarLynda R.,et al."Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes".BMC SYSTEMS BIOLOGY 7(2013). |
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