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Our calculations use methods from graph theory.
This allows the adaptation of methods from graph theory to structure graphs and their direct use with kernel learning algorithms.
Among a number of possible methods from Graph Theory [91, 92, 153, 154], we opted for a novel technique based on the extraction of Simmelian backbones [155], due to its efficiency to analyze complex networks with unweighted edges.
Further, it allows for the use of methods from graph theory and complex network analysis, which strengthen our approach.
Recently, junction tree methods from graph theory were used to describe an efficient algorithm to compute marginal posterior genotype probabilities for pedigrees with loops [ 10].
Combining methods from graph theory and machine learning, we have shown here that evolutionary pressures not only affects gene and protein sequences, but also specific details of the complex wiring of functional modules in the cell.
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The lumping makes use of efficient methods from graph-theory and ϵ-decomposition and is derived and exemplified on two published models for fluorescence emission in photosynthesis.
Finally, since such network design problems belong to the class of NP-hard problems, a novel heuristic solution method is developed based on a new solution representation method derived from graph theoretic view to the structure of the studied problem.
In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network.
However, comparing the relative performance of the methods from such graphs becomes subjective.
And then I use the method from the graph to add the edge.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com