ArticleBMC bioinformatics2018
Bayesian graphical models for computational network biology.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- Exploring cooperation among social pathogens: a computational perspective.FEMS microbiology reviews · 2026Review
- Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.Cell systems · 2024Article
- Markov Field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.bioRxiv : the preprint server for biology · 2024Article
- Article
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- Gaussian Bayesian network comparisons with graph ordering unknown.Computational statistics & data analysis · 2021Article
- Review
- Selected research articles from the 2017 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC).BMC bioinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundComputational network biology is an emerging interdisciplinary research area. Among many other network approaches, probabilistic graphical models provide a comprehensive probabilistic characterization of interaction patterns between molecules and the associated uncertainties.
resultsIn this article, we first review graphical models, including directed, undirected, and reciprocal graphs (RG), with an emphasis on the RG models that are curiously under-utilized in biostatistics and bioinformatics literature. RG's strictly contain chain graphs as a special case and are suitable to model reciprocal causality such as feedback mechanism in molecular networks. We then extend the RG approach to modeling molecular networks by integrating DNA-, RNA- and protein-level data. We apply the extended RG method to The Cancer Genome Atlas multi-platform ovarian cancer data and reveal several interesting findings.
conclusionsThis study aims to review the basics of different probabilistic graphical models as well as recent development in RG approaches for network modeling. The extension presented in this paper provides a principled and efficient way of integrating DNA copy number, DNA methylation, mRNA gene expression and protein expression.
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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.