Joint Multiple Testing Procedures for Graphical Model Selection with Applications to Biological Networks
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Summary
A multiple testing framework useful when testing for edge inclusion during graphical model selection is proposed and implemented and it is observed that the use of more sophisticated, modular approaches to multiple testing allows one to identify greater numbers of edges when approximating an undirected graphical model using a 0-1 graph.
- Type
- article
- Published
- 2009-01-01
- Cited by
- 7
- References
- 52
- Access
- Open access
- OpenAlex
- https://openalex.org/W82995012
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:55339334
Keywords
Graphical model, Conditional independence, Model selection, Mathematics, Null distribution
References
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- An integrative genomics approach to the reconstruction of gene networks in segregating populations
- Distinct Light-Mediated Pathways Regulate the Biosynthesis and Exchange of Isoprenoid Precursors during Arabidopsis Seedling Development Article, publication date, and citation information can be found at www.plantcell.org/cgi/doi/10.1105/tpc.016204.
- Gene Systems Network Inferred from Expression Profiles in Hepatocellular Carcinogenesis by Graphical Gaussian Model
- Choice of a null distribution in resampling-based multiple testing
- Exceedance Control of the False Discovery Proportion
- From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
- System for Automatically Inferring a Genetic Netwerk from Expression Profiles
- Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks
- A stochastic process approach to false discovery control
- A SINful approach to Gaussian graphical model selection
Cited by
- Resampling-Based Multiple Hypothesis Testing with Applications to Genomics: New Developments in the R/Bioconductor Package multtest
- Regularized Learning of High-dimensional Sparse Graphical Models
- A comparative study of Gaussian Graphical Model approachesfor genomic data
- A comparative study of covariance selection models for the inference of gene regulatory networks
- Arabidopsis GERANYLGERANYL DIPHOSPHATE SYNTHASE 11 is a hub isozyme required for the production of most photosynthesis-related isoprenoids.
- Extending Noisy-Max Gates to Bidirectional Models ⋆
- Statistical Applications in Genetics and Molecular Biology Assessing Modularity Using a Random Matrix Theory Approach
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