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• W3025592215 startingPage "597" @default.
• W3025592215 abstract "Abstract Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods." @default.
• W3025592215 created "2020-05-21" @default.
• W3025592215 creator A5008116286 @default.
• W3025592215 creator A5032384046 @default.
• W3025592215 creator A5055050168 @default.
• W3025592215 date "2020-07-01" @default.
• W3025592215 modified "2023-10-14" @default.
• W3025592215 title "Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection" @default.
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• W3025592215 doi "https://doi.org/10.1534/genetics.120.303186" @default.
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• W3025592215 hasPublicationYear "2020" @default.
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