ArticleAnnals of statistics2012
MODELING DEPENDENT GENE EXPRESSION.
Article in Annals of statistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA.The annals of applied statistics · 2025Article
- Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases.Journal of applied statistics · 2024Article
- Bayesian graphical models for computational network biology.BMC bioinformatics · 2018Article
- Detecting differential patterns of interaction in molecular pathways.Biostatistics (Oxford, England) · 2015Article
- Graph hierarchies for phylogeography.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2013Article
- An Integrative Bayesian Modeling Approach to Imaging Genetics.Journal of the American Statistical Association · 2013Article
- MODELING DEPENDENT GENE EXPRESSION.Annals of statistics · 2012Article
- On differential gene expression using RNA-Seq data.Cancer informatics · 2011Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.