ArticleBiometrical journal. Biometrische Zeitschrift2017
An empirical Bayes approach to network recovery using external knowledge.
Article in Biometrical journal. Biometrische Zeitschrift, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 1 citations in OpenAlex.
- Identifying large-scale interaction atlases using probabilistic graphs and external knowledge.Journal of clinical and translational science · 2022Article
- Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data.Biometrical journal. Biometrische Zeitschrift · 2021Article
- F-MAP: A Bayesian approach to infer the gene regulatory network using external hints.PloS one · 2017Article
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Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
Abstract
Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data.
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Registered trials
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