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      From big data to important information

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      Complexity
      Wiley

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          Most cited references72

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          Renormalization Group and Critical Phenomena. I. Renormalization Group and the Kadanoff Scaling Picture

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            Reaction-diffusion model as a framework for understanding biological pattern formation.

            The Turing, or reaction-diffusion (RD), model is one of the best-known theoretical models used to explain self-regulated pattern formation in the developing animal embryo. Although its real-world relevance was long debated, a number of compelling examples have gradually alleviated much of the skepticism surrounding the model. The RD model can generate a wide variety of spatial patterns, and mathematical studies have revealed the kinds of interactions required for each, giving this model the potential for application as an experimental working hypothesis in a wide variety of morphological phenomena. In this review, we describe the essence of this theory for experimental biologists unfamiliar with the model, using examples from experimental studies in which the RD model is effectively incorporated.
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              Long-range correlations in nucleotide sequences.

              DNA sequences have been analysed using models, such as an n-step Markov chain, that incorporate the possibility of short-range nucleotide correlations. We propose here a method for studying the stochastic properties of nucleotide sequences by constructing a 1:1 map of the nucleotide sequence onto a walk, which we term a 'DNA walk'. We then use the mapping to provide a quantitative measure of the correlation between nucleotides over long distances along the DNA chain. Thus we uncover in the nucleotide sequence a remarkably long-range power law correlation that implies a new scale-invariant property of DNA. We find such long-range correlations in intron-containing genes and in nontranscribed regulatory DNA sequences, but not in complementary DNA sequences or intron-less genes.
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                Author and article information

                Journal
                Complexity
                Complexity
                Wiley
                10762787
                November 2016
                November 2016
                April 25 2016
                : 21
                : S2
                : 73-98
                Affiliations
                [1 ]New England Complex Systems Institute; 210 Broadway, Suite 101 Cambridge Massachusetts 02139
                Article
                10.1002/cplx.21785
                02d2d466-1474-4459-a090-6a6fe2fef7fe
                © 2016

                http://doi.wiley.com/10.1002/tdm_license_1.1

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