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      mgm: Structure Estimation for Time-Varying Mixed Graphical Models in high-dimensional Data

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          Abstract

          We present the R-package mgm for the estimation of both stationary and time-varying mixed graphical models and mixed vector autoregressive models in high-dimensional data. Variables of mixed type (continuous, count, categorical) are ubiquitous in datasets in many disciplines, however, available methods cannot incorporate (nominal) categorical variables and suffer from possible information loss due to transformations of non-Gaussian continuous variables. In addition, we extend both models to the time-varying case in which the true model changes over time, under the assumption that change is a smooth function of time. Time-varying models offer a rich description of temporally evolving systems as they provide information about organizational processes, information diffusion, vulnerabilities and the potential impact of interventions. Next to introducing the theory of the implemented methods and explaining the software package, we provide performance benchmarks and applications to two medical datasets.

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          Most cited references 6

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          Statistical mechanics of complex networks

          Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks is governed by robust organizing principles. Here we review the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, we discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, as well as the interplay between topology and the network's robustness against failures and attacks.
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            Gene expression during the life cycle of Drosophila melanogaster.

            Molecular genetic studies of Drosophila melanogaster have led to profound advances in understanding the regulation of development. Here we report gene expression patterns for nearly one-third of all Drosophila genes during a complete time course of development. Mutations that eliminate eye or germline tissue were used to further analyze tissue-specific gene expression programs. These studies define major characteristics of the transcriptional programs that underlie the life cycle, compare development in males and females, and show that large-scale gene expression data collected from whole animals can be used to identify genes expressed in particular tissues and organs or genes involved in specific biological and biochemical processes.
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              Estimating time-varying networks

               ,  ,   (2010)
              Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed \(l_1\)-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks. For real data sets, we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US Senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from the microarray time course.
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                Author and article information

                Journal
                2015-10-23
                2016-04-26
                Article
                1510.06871

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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