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The MULTIMOORA method also found wide application areas like the MOORA method.
This method also found a significantly higher spotted hyaena and African golden wolf abundance in high human density areas.
The scientists pursuing that method also found their guesswork limited by "the effect of the unknown unknowns" which "made it more difficult to produce a reliable upper bound on the flow rate".
Most of the categories found to contain the most differentially expressed genes by one given method were also found by other methods, but each method also found unique categories based on these criteria.
Similar log-likelihoods were found, independently, by PROML from the PHYLIP phylogenetics package [ 122], which allows a star tree as a constrained topology in a maximum likelihood analysis (this method also found AIC scores similar to the Bayes factors reported in Tables 3 and 4).
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The proposed method also finds solutions faster than the other evolutionary approaches mentioned above.
This method also finds out the amount of and the specific data that can be used for the training phase based on the complexity of the problem.
The analyzed data has also been analyzed at Jyväskylä University by other data mining methods [1]; one objective of this study is to investigate whether the GUHA method also finds dependencies that are not reported in the study [1].
The t-plot method also finds that there is a minimum in the pore volume and size at pH = 8 and that there is a general decrease in pore volume and size as pH increases.
Our method also finds the exo-acting cellobiohydrolases GH7 and GH48 to be less important.
This method also finds several codons in Physalis that are under significantly greater positive selection than Solanum as shown by contrasts of mean dN/dS values at these particular sites.
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