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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 8520480, 10 pages
Research Article

Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information

1Department of Statistics, Keimyung University, Daegu, Republic of Korea
2The Institute of Natural Science, Keimyung University, Daegu, Republic of Korea
3Department of Statistics, Korea University, Seoul, Republic of Korea
4Graduate School of Information Security, Korea University, Seoul, Republic of Korea
5School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea

Correspondence should be addressed to ByungYong Lee; moc.liamg@101901mor and SungWon Han;

Received 31 October 2016; Revised 20 February 2017; Accepted 6 March 2017; Published 12 April 2017

Academic Editor: Hongmei Zhang

Copyright © 2017 SungHwan Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.