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In addition, the marker-based approach of supervised segmentation which suffers from an issue of marker selection does not exist in this new method.
Thus, the number of PIC is directly proportional to the number of sequenced bp and thus on this phylogenetic level marker selection does not seem to be crucial in terms of phylogenetic content.
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The random selection of the markers in the proposed methods is at a higher speed compared to the marker selection done by connected component analysis [22].
First, successful integration of the marker gene (and positive selection) does not ensure integration of the gene of interest.
We considered substitution rates previously estimated from our intron and mitochondrial mammalian trees as mutation rates to be used in the species tree, under the assumption that the markers are neutral and selection did not significantly affect them [ 17, 18].
If natural selection did favor the curious, then we should see deeply ingrained markers in our modern day brains evidence of a hardwired system that would cause us to eagerly anticipate rewards and reinforce our curious impulses.
Inference based on this method does not depend on the modeling assumptions and also requires no special marker selection criteria.
Yet, these results, in and of themselves, do not alleviate concerns related to high-grading bias associated with marker selection.
A final weakness is that the chip does not cover the whole genome, and depends on the power of the initial GWAS studies for its marker selection.
However, marker selection is still a fundamental challenge.
The following describes our marker selection criteria.
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