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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Maruyama, O. Shikita, S. |
| Copyright Year | 2014 |
| Description | Author affiliation: Grad. Sch. of Math., Kyushu Univ., Fukuoka, Japan (Shikita, S.) || Inst. of Math. for Ind., Kyushu Univ., Fukuoka, Japan (Maruyama, O.) |
| Abstract | The inference of gene association networks from gene expression profiles is an important approach to elucidate various cellular mechanisms. However, there exists a problematic issue that the number of samples is relatively small than that of genes. A promising approach to this problem will be to design regularization terms for characteristic network structures like sparsity and scale-freeness and optimize a scoring function including those regularization terms. The inference problem for gene association networks is often formulated as the problem of estimating the inverse covariance matrix of a Gaussian distribution from its samples. For this Bayesian inference problem, we propose a novel scale-free structure prior and devise a sampling method for optimizing a posterior probability including the prior. In a simulation study, scale-free graphs of 30 and 100 nodes are generated by the Barabási-Albert model, and the proposed method is shown to outperform another method which also use a scale-free regularization term. Our method is also applied to real gene expression profiles, and the resulting graph shows biologically meaningful features. Thus, we empirically conclude that our scale-free structure prior is effective in Bayesian inference of Gaussian graphical models. |
| Starting Page | 131 |
| Ending Page | 138 |
| File Size | 674697 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999141 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Graphical models Simulated annealing Gaussian distribution Bayes methods Gene expression Proposals Covariance matrices |
| Content Type | Text |
| Resource Type | Article |
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