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• W2966517286 abstract "SUMMARY Motivated by the study of the molecular mechanism underlying type 1 diabetes with gene expression data collected from both patients and healthy controls at multiple time points, we propose a hybrid Bayesian method for jointly estimating multiple dependent Gaussian graphical models with data observed under distinct conditions, which avoids inversion of high-dimensional covariance matrices and thus can be executed very fast. We prove the consistency of the proposed method under mild conditions. The numerical results indicate the superiority of the proposed method over existing ones in both estimation accuracy and computational efficiency. Extension of the proposed method to joint estimation of multiple mixed graphical models is straightforward." @default.
• W2966517286 created "2019-08-13" @default.
• W2966517286 creator A5070413478 @default.
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• W2966517286 date "2019-08-02" @default.
• W2966517286 modified "2023-10-14" @default.
• W2966517286 title "Fast hybrid Bayesian integrative learning of multiple gene regulatory networks for type 1 diabetes" @default.
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• W2966517286 doi "https://doi.org/10.1093/biostatistics/kxz027" @default.
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