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Functional association networks as priors for gene regulatory network inference
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Cancer and Vascular Biology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Cancer and Vascular Biology.
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2014 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 12, 130-138 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2014. Vol. 30, no 12, 130-138 p.
National Category
Bioinformatics (Computational Biology) Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-229450DOI: 10.1093/bioinformatics/btu285ISI: 000338109200016OAI: oai:DiVA.org:uu-229450DiVA: diva2:736623
Available from: 2014-08-07 Created: 2014-08-07 Last updated: 2017-12-05Bibliographically approved

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Nordling, TorbjörnNelander, Sven

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