ArticleCancer informatics2014
Integrative bayesian network analysis of genomic data.
Article in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
The trial behind it
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
10 citing papers in PubMed.
- The expression of insulin signaling and N-methyl-D-aspartate receptor genes in areas of gray matter atrophy is associated with cognitive function in type 2 diabetes.Journal of Alzheimer's disease : JAD · 2025Article
- Erratum: Characteristics and Comorbidities Influencing Mortality Risk Among Hereditary Angioedema Patients.Journal of health economics and outcomes research · 2025Article
- Using empirical biological knowledge to infer regulatory networks from multi-omics data.BMC bioinformatics · 2022Article
- Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.Frontiers in genetics · 2019Review
- Bayesian graphical models for computational network biology.BMC bioinformatics · 2018Article
- Significance evaluation in factor graphs.BMC bioinformatics · 2017Article
- Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.Statistical modelling · 2017Article
- SIFORM: shared informative factor models for integration of multi-platform bioinformatic data.Bioinformatics (Oxford, England) · 2016Article
- FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.PLoS computational biology · 2016Article
- Exploring the Role of MicroRNAs and Associated Proteins in Multiple Sclerosis.Basic and clinical neuroscienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient's clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.
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What OpenQuestion holds
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.