Functional association networks as priors for gene regulatory network inference

Bioinformatics
Matthew StudhamErik L L Sonnhammer

Abstract

Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data.

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Citations

Nov 8, 2014·Molecular BioSystems·Andreas TjärnbergErik L L Sonnhammer
Oct 31, 2016·Nucleic Acids Research·Alireza F Siahpirani, Sushmita Roy
Nov 13, 2015·Science Translational Medicine·Mika GustafssonMikael Benson
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Feb 15, 2018·Proceedings of the National Academy of Sciences of the United States of America·Justin D FinkleNeda Bagheri
Nov 23, 2017·Nucleic Acids Research·Christoph OgrisErik L L Sonnhammer
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Jul 24, 2020·Journal of Bioinformatics and Computational Biology·Stephanie Kamgnia Wonkap, Gregory Butler
Dec 8, 2016·Nucleic Acids Research·Damian SzklarczykChristian von Mering
Jul 18, 2018·Computational and Mathematical Methods in Medicine·Francisco Gómez-VelaJosé Luis Vázquez-Noguera

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Saccharomyces cerevisiae
MRNA Differential Display
Gene Modules
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Data Types - String
Gene Regulatory Networks
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