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 involved players (e.g. genes) and much fewer biological replicates available for analysis. Herein, we proposed a new statistical method of estimating the number of erroneous edges  that strongly enhances the commonly used inference approaches. This method is based on  special relationship between correlation and causality, and allows to identify and to remove approximately half of  erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we established a mathematical foundation for our method. Analyzing real biological datasets, we found a strong correlation between the results of our method and the commonly used false discovery rate (FDR) technique. Furthermore, the simulation analysis demonstrates that in large networks , our new method provides a more precise estimation of the proportion of erroneous links than FDR.
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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and much fewer
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proposed
propose
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in reconstructed networks
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to identify and to remove
for the identification and to removal of
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all
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establish
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real
existing
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find
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in large networks ,
with large networks
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precise estimation of the proportion of erroneous links
accurate estimate of network error