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Unexpected links reflect the noise in networks

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https://ir.library.oregonstate.edu/concern/articles/zw12z6694

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  • Gene regulatory networks are commonly used for modeling biological processes and revealing underlying molecular mechanisms. The reconstruction of gene regulatory networks from observational data is a challenging task, especially considering the large number of players (e.g. genes) involved and the small number of biological replicates available for analysis. Herein, we propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between correlation and causality, and allows for the identification and to removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and the commonly used false discovery rate (FDR) technique. Furthermore, simulation analysis demonstrates that with large networks our new method provides a more accurate estimate of network error than FDR.
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  • 11
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