ArticleBiostatistics (Oxford, England)2015
Detecting differential patterns of interaction in molecular pathways.
Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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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Who cites it
9 citing papers in PubMed.
- SELF-Tree: An Interpretable Model for Multivariate Causal Direction Heterogeneity Analysis.Psychometrika · 2026Article
- 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 graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure.Journal of machine learning research : JMLR · 2022Article
- A random covariance model for bi-level graphical modeling with application to resting-state fMRI data.Biometrics · 2021Article
- Bayesian Joint Modeling of Multiple Brain Functional Networks.Journal of the American Statistical Association · 2021Article
- Bayesian Graphical Regression.Journal of the American Statistical Association · 2019Article
- Bayesian graphical models for computational network biology.BMC bioinformatics · 2018Article
- A Bayesian Approach for Learning Gene Networks Underlying Disease Severity in COPD.Statistics in biosciences · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We consider statistical inference for potentially heterogeneous patterns of association characterizing the expression of bio-molecular pathways across different biologic conditions. We discuss a modeling approach based on Gaussian-directed acyclic graphs and provide computational and methodological details needed for posterior inference. Our application finds motivation in reverse phase protein array data from a study on acute myeloid leukemia, where interest centers on contrasting refractory versus relapsed patients. We illustrate the proposed method through both synthetic and case study data.
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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.