Gene network inference by fusing data from diverse distributions.
Bottom Line: In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models.Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset.Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies.
Affiliation: Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.Show MeSH
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Mentions: We have developed a novel approach, called FuseNet, for inference of undirected networks from a number of high-dimensional datasets (Fig. 1). Our approach builds upon recent theoretical results about Markov networks (Yang et al., 2012, 2013) and, unlike the previous works in Markov modeling, can be applied to settings where data arise from multiple related but otherwise nonidentical distributions. To achieve this level of modeling flexibility, we represent model parameters with latent factors. FuseNet implements data fusion through sharing of latent factors that are common to all datasets and distributions, and handles data diversity through inference of factors specific to a particular dataset.Fig. 1.
Affiliation: Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.