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• W165803399 abstract "AbstractNetworktheoryistodayacentraltopicincomputationalsystemsbiology as a framework to understand and reconstruct relationsamong biological components. For example, constructing networksfrom a gene expression dataset provides a set of possible hypothesesexplaining connections among genes, vital knowledge to advancingour understanding of living organisms as systems. Here we brieflysurvey aspects atthe intersectionof information theoryand networkbiology. We show that Shannon’s information entropy, Kolmogorovcomplexity and algorithmic probability quantify different aspectsof biological networks at the interplay of local and global patterndetection. Weprovideapproximationstothealgorithmicprobabilityand Kolmogorov complexity of motifs connected to the asymptotictopologicalpropertiesofnetworks.Keywords: information theory, complex networks, network mo-tifs, Kolmogorov complexity, algorithmic randomness, algorithmicprobability,biologicalnetworks. Supplemental material with Mathematica source code is available at http://www.complexitycalculator.com/zeniltegnerWiley2013.zip" @default.
• W165803399 created "2016-06-24" @default.
• W165803399 creator A5003718950 @default.
• W165803399 creator A5035252549 @default.
• W165803399 date "2014-01-15" @default.
• W165803399 modified "2023-10-05" @default.
• W165803399 title "Information Theory, Kolmogorov Complexity and Algorithmic Probability in Network Biology" @default.
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• W165803399 hasPublicationYear "2014" @default.
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