Abstract
Genes interact with each other in complex networks that enable the processing of information inside the cell. For an understanding of the cellular functions, the identification of the gene regulatory networks is essential. We present a novel reverse-engineering method to recover networks from gene expression measurements. Our approach is based on Boolean networks, which require the assignment of the label “expressed” or “not expressed” to an individual gene. However, techniques like microarray analyses provide real-valued expression values, consequently the continuous data have to be binarized. Binarization is often unreliable, since noise on gene expression data and the low number of temporal measurement points frequently lead to an uncertain binarization of some values. Our new approach incorporates this uncertainty in the binarized data for the inference process. We show that this new reconstruction approach is less influenced by noise which is inherent in these biological systems.
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© 2012 Springer-Verlag Berlin Heidelberg
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Hopfensitz, M., Maucher, M., Kestler, H.A. (2012). Fuzzy Boolean Network Reconstruction. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_27
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DOI: https://doi.org/10.1007/978-3-642-24466-7_27
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