Seeded Bayesian Networks: Constructing genetic networks from microarray data

By A. Djebbari, John Quackenbush. BMC Systems Biology, 2008

387 citations recorded by the source corpus.

BackgroundDNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results.ResultsThrough a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data.ConclusionThe use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7cb1bfa725ad08e3a593b67d364136dc0df1951b

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Citations in the corpus

Network visualization and analysis of gene expression data using BioLayout Express3D (2009, methodology): “Again using the macrophage activation pathway example of Raza et al. 26 .”
The Systems Biology Graphical Notation (2009, background): “Several proposals followed to describe process diagrams, not only with standard symbols but also defined grammars 20–23 .”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, background): “diabetes [73], chronic fatigue [74], other diseases [75, 76] and tis-”