 Bayesian inference of hub nodes across multiple networksJunghi Kim, Kim‐Anh Do, Min Jin Ha and Christine B. Peterson Biometrics, 2019, vol. 75, issue 1, 172-182 Abstract: Hub nodes within biological networks play a pivotal role in determining phenotypes and disease outcomes. In the multiple network setting, we are interested in understanding network similarities and differences across different experimental conditions or subtypes of disease. The majority of proposed approaches for joint modeling of multiple networks focus on the sharing of edges across graphs. Rather than assuming the network similarities are driven by individual edges, we instead focus on the presence of common hub nodes, which are more likely to be preserved across settings. Specifically, we formulate a Bayesian approach to the problem of multiple network inference which allows direct inference on shared and differential hub nodes. The proposed method not only allows a more intuitive interpretation of the resulting networks and clearer guidance on potential targets for treatment, but also improves power for identifying the edges of highly connected nodes. Through simulations, we demonstrate the utility of our method and compare its performance to current popular methods that do not borrow information regarding hub nodes across networks. We illustrate the applicability of our method to inference of co‐expression networks from The Cancer Genome Atlas ovarian carcinoma dataset. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:75:y:2019:i:1:p:172-182 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.