How can Gaussian graphical Networks Models help in exploring Ovarian cancer data?
Article: Gaussian graphical models with applications to omics analysesAuthors and Year: Katherine H. Shutta, Roberta De Vito, Denise M. Scholtens, Raji Balasubramanian 2022Journal: Statistics in Medicine Review Prepared by: Sanou Edmond, Postdoc in BiostatisticsNuclear Safety and Radiation Protection Authority (ASNR) As scientists collect more detailed biological data, they use networks to understand how molecules in the body interact and how these interactions relate to disease. This type of data, known as omics, includes information about genes (genomics), proteins (proteomics), and other molecules. These networks can help find genes linked to illness and even suggest possible treatment options. Statisticians help by using tools that highlight which molecules are directly connected. In their tutorial “Gaussian Graphical Models with Applications to Omics Analyses,” Shutta et al. recommend using a method called Gaussian Graphical Models (GGMs) to study these connections. GGMs help draw simple, clear maps of how molecules relate to each other. The authors…