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

Osamu Maruyama, Shota Shikita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
EditorsHuiru Zheng, Xiaohua Tony Hu, Daniel Berrar, Yadong Wang, Werner Dubitzky, Jin-Kao Hao, Kwang-Hyun Cho, David Gilbert
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages131-138
Number of pages8
ISBN (Electronic)9781479956692
DOIs
Publication statusPublished - Dec 29 2014
Event2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 - Belfast, United Kingdom
Duration: Nov 2 2014Nov 5 2014

Publication series

NameProceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

Other

Other2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
Country/TerritoryUnited Kingdom
CityBelfast
Period11/2/1411/5/14

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

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