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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
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Gene interaction networks from donor and failing heart gene expression

Authors: Pablo Cordero; Victoria N Parikh; Elizabeth Chin; Euan Ashley;

Gene interaction networks from donor and failing heart gene expression

Abstract

These are the gene-gene interaction networks obtained from gene expression microarray data in the paper "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure" by Cordero, Parikh, et al. The dataset contains the following items: * networks.zip: Compressed weighted matrices, in numpy format, for various methods in heart failure and donor cohorts, which are in the file name (ARACNE, JGL [Joint Graphical Lasso], Z-score, Pearson correlation, CLR [Context Likelihood of Relatedness]) * wgcna.zip: Compressed weighted matrices, in numpy format, for WGCNA. These are the networks used in the paper. * network_gene_names.txt: Gene names for rows and columns of the gene networks in networks.zip * wgcna Construction of the networks were done as follows for each method: * For Pearson, Z-score, and CLR, we simply computed each relevant statistic in a pairwise manner, no thresholds were applied. * For ARACNE, we performed 500 bootstrapped iterations and searched the MI cutoff automatically using default parameters. * For JGL we performed an Akaike Information Criteria guided model selection by scanning the sparsity and group difference L1 penalty strengths.

Related Organizations
Keywords

cardiovascular disease, gene networks, heart failure, eQTL, network analysis, co-expression

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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