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The cladistic approach deals with this problem by generating an ensemble or consensus cladogram that is consistent with the largest number of characters and therefore requires the smallest number of evolutionary changes to account for the distribution of character states among the taxa.
Given experimental observations of such sets, we infer the underlying network connecting these entities by generating an ensemble of networks consistent with the data.
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To this end, we present a method by which one generates an ensemble of 3D realizations using one or very few 2D images by an iterative multiscale approach.
A similar and possibly more accurate version of this ensemble could be made by generating a second Asm model using a shorter token length of 50 vs. 55 to pick up additional missing predictions.
The algorithm works by generating a random sample, of size N g, of the ensemble of networks G ens consistent with the data.
Random forests generates an ensemble of trees by treating the tree parameters as fixed but the data as random — data and predictors are sampled.
In order to give flexibility in future predictors predictand relationships and to account the sensitivity in model parameters, it is also proposed to generate an ensemble of outputs by identifying various plausible model parameter combinations.
The BC3Net generates an ensemble of C3Net networks from bootstrap datasets, i.e., by sampling a dataset with replacement, that are subsequently aggregated to a weighted network.
It could also be used to generate an ensemble of solutions that would be further refined and assessed by flexible fitting methods.
One critical part of ensemble docking is to generate an ensemble of peptide 3D models that include protein-bound peptide conformations, so that the biologically active ones can be selected by the protein during ensemble docking [3, 23, 28].
The developed method generates an ensemble of facies fields that honor the facies distribution as described by a probability field.
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