A scale-free structure prior for Bayesian inference of Gaussian graphical models

Osamu Maruyama, Shota Shikita

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
編集者Huiru Zheng, Xiaohua Tony Hu, Yadong Wang, Jin-Kao Hao, David Gilbert, Daniel Berrar, Kwang-Hyun Cho, Werner Dubitzky
出版社Institute of Electrical and Electronics Engineers Inc.
ページ131-138
ページ数8
ISBN(電子版)9781479956692
DOI
出版ステータス出版済み - 12 29 2014
イベント2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 - Belfast, 英国
継続期間: 11 2 201411 5 2014

出版物シリーズ

名前Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

その他

その他2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
Country英国
CityBelfast
Period11/2/1411/5/14

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

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