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Supporting this latter hypothesis was the inference of hybridization between L. timidus and L. mandshuricus as well as L. timidus and L. capensis (see below).
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An obvious application is the inference of genomic regulatory networks from sample microarray data.
An important problem in bioinformatics is the inference of gene regulatory networks (GRNs) from expression profiles.
One aspect of such reasoning is the inference of function from the structure of the artifacts one encounters.
One is ancestral genome, which is the inference of the genetic makeup of a common ancestor of two or more contemporary genomes.
The distinctive feature of this approach is the inference of the structure of an unknown compound that is absent from spectral libraries, i.e. without employing reference structures and their associated spectra.
The key computational problem related to these models is the inference of reticulate networks.
So, is the inference of a population bottleneck in C. rubella incorrect?
A fundamental challenge in analyzing these data is the inference of protein machineries.
A major task is the inference of regulatory dependencies between genes on the basis of such data.
One difficulty in analyzing evolutionary constraints in noncoding DNA is the inference of the correct sequence alignment.
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