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We show that other known methods perform far worse and have much more variability in their accuracy.
We show that other known methods perform far worse and have much more variability in their accuracy, both across anatomical components and between their sensitivity (i.e. the ability to correctly classify images that contain gene expression) and specificity (i.e. the ability to correctly classify images that do not contain gene expression).
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In terms of the functional coherence of the detected modules, the combined method performs far better than sequence-based alignment.
However, GIFT perform far better than baseline methods in all cases.
Using the mathematical methods that allow Dactyl to learn, researchers believe they can train robotic hands and other machines to perform far more complex tasks.
However, when M is small, they perform far below capacity.
The supply of adequate amounts of safe, clean, usable energy can only be met by a suite of methods, each of which is performing far better than at present.
For larger sample sizes, the nonparametric methods perform just as well as the parametric methods when the correct distribution is used for modeling, with far better performance when an incorrect distribution is applied.
In terms of stability, QMOM and EQMOM performed far better than the Maximum Entropy method.
This 11 taxon phylogeny combines elements of the balanced and pectinate topologies performed far better than either the NJ with Euclidean distance or parsimony methods.
The Australian model has performed far better.
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CEO of Professional Science Editing for Scientists @ prosciediting.com