Functional association networks as priors for gene regulatory network inference.

Autor:	Studham ME; Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden., Tjärnberg A; Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden., Nordling TE; Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden., Nelander S; Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden., Sonnhammer EL; Stockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, SE-171 65 Solna, Sweden, Department of Biochemistry and Biophysics, Stockholm University, SE-106 91 Stockholm, Sweden, Department of Immunology, Genetics and Pathology, Uppsala University, Rudbeck Laboratory, SE-751 05 Uppsala, Sweden and Swedish eScience Research Center, SE-100 44 Stockholm, Sweden.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2014 Jun 15; Vol. 30 (12), pp. i130-8.
DOI:	10.1093/bioinformatics/btu285
Abstrakt:	Motivation: 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. Results: 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. (© The Author 2014. Published by Oxford University Press.)
Databáze:	MEDLINE
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