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• W2980454921 endingPage "194418" @default.
• W2980454921 startingPage "194418" @default.
• W2980454921 abstract "Gaussian Graphical Models (GGMs) are tools to infer dependencies between biological variables. Popular applications are the reconstruction of gene, protein, and metabolite association networks. GGMs are an exploratory research tool that can be useful to discover interesting relations between genes (functional clusters) or to identify therapeutically interesting genes, but do not necessarily infer a network in the mechanistic sense. Although GGMs are well investigated from a theoretical and applied perspective, important extensions are not well known within the biological community. GGMs assume, for instance, multivariate normal distributed data. If this assumption is violated Mixed Graphical Models (MGMs) can be the better choice. In this review, we provide the theoretical foundations of GGMs, present extensions such as MGMs or multi-class GGMs, and illustrate how those methods can provide insight in biological mechanisms. We summarize several applications and present user-friendly estimation software. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony." @default.
• W2980454921 created "2019-10-25" @default.
• W2980454921 creator A5002232081 @default.
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• W2980454921 creator A5084245470 @default.
• W2980454921 creator A5091597117 @default.
• W2980454921 date "2020-06-01" @default.
• W2980454921 modified "2023-10-16" @default.
• W2980454921 title "Gaussian and Mixed Graphical Models as (multi-)omics data analysis tools" @default.
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• W2980454921 doi "https://doi.org/10.1016/j.bbagrm.2019.194418" @default.
• W2980454921 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7166149" @default.
• W2980454921 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31639475" @default.
• W2980454921 hasPublicationYear "2020" @default.
• W2980454921 type Work @default.
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