Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data-Reference-Cited by-同舟云学术

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Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data

Published:2018-10-02 Issue: Volume:9 Page:
ISSN:1664-8021
Container-title:Frontiers in Genetics
language:
Short-container-title:Front. Genet.

Author:

Li Xiang,Xie Shanghong,McColgan Peter,Tabrizi Sarah J.,Scahill Rachael I.,Zeng Donglin,Wang Yuanjia

Funder

National Institute of Neurological Disorders and Stroke

National Institute of General Medical Sciences

Wellcome Trust

Publisher

Frontiers Media SA

Subject

Genetics(clinical),Genetics,Molecular Medicine

Reference38 articles.

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3. Brain network local interconnectivity loss in aging APOE-4 allele carriers;Brown;Proc. Natl. Acad. Sci. U.S.A.,2011

4. Covariate-adjusted precision matrix estimation with an application in genetical genomics;Cai;Biometrika,2013

5. A sparse Ising model with covariates;Cheng;Biometrics,2014

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1. In reference to ‘Directed acyclic graphs for clinical research: a tutorial’;Journal of Minimally Invasive Surgery;2023-12-15

2. Counterfactual reasoning in space and time: Integrating graphical causal models in computational movement analysis;Transactions in GIS;2023-09-12

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