Bayesian graphical models for modern biological applications
| dc.citation.firstpage | 197 | |
| dc.citation.journalTitle | Statistical Methods & Applications | |
| dc.citation.lastpage | 225 | |
| dc.citation.volumeNumber | 31 | |
| dc.contributor.author | Ni, Yang | |
| dc.contributor.author | Baladandayuthapani, Veerabhadran | |
| dc.contributor.author | Vannucci, Marina | |
| dc.contributor.author | Stingo, Francesco C. | |
| dc.date.accessioned | 2023-01-27T14:47:33Z | |
| dc.date.available | 2023-01-27T14:47:33Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging. | |
| dc.identifier.citation | Ni, Yang, Baladandayuthapani, Veerabhadran, Vannucci, Marina, et al.. "Bayesian graphical models for modern biological applications." <i>Statistical Methods & Applications,</i> 31, (2022) Springer Nature: 197-225. https://doi.org/10.1007/s10260-021-00572-8. | |
| dc.identifier.digital | s10260-021-00572-8 | |
| dc.identifier.doi | https://doi.org/10.1007/s10260-021-00572-8 | |
| dc.identifier.uri | https://hdl.handle.net/1911/114289 | |
| dc.language.iso | eng | |
| dc.publisher | Springer Nature | |
| dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Bayesian graphical models for modern biological applications | |
| dc.type | Journal article | |
| dc.type.dcmi | Text | |
| dc.type.publication | publisher version |
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