Titlebook: Gene Regulatory Networks; Methods and Protocol Guido Sanguinetti,Vân Anh Huynh-Thu Book 2019 Springer Science+Business Media, LLC, part of

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Folsäure und Ursodesoxycholsäure our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of co

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Galenik der Mesalazine — Klinische Relevanz?based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametr

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https://doi.org/10.1007/978-3-642-85915-1ent algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensu

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E. Lütjen-Drecoll,P. Steuhl,W. H. Arnoldf coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, us

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Conference proceedings 1987Latest editionrs. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in an ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We

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https://doi.org/10.1007/978-3-662-63596-4 from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates