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The other four methods perform about equally well in the Osmotic shock response data set, but in the DNA damage data set our methods outperform HMoF as well.
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However, we noticed that the two methods performed about equally well.
Finally, our method performs about as well as surrogate variable analysis (SVA) when technical error is of large magnitude, and better than SVA when technical error is of smaller (but still realistic) magnitude.
This suggests that the methods performed about the same in the forecasting of seasonal epidemics.
2. When noise increased to a large extent, the proposed method was able to perform about twice as well as random walk approach did.
Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths.
Various methods have been developed for building species distribution models but it is not well known how these methods perform under various assumptions about the form of the underlying species response.
This last results suggests that, even with the good insights provided by this work about which methods perform better in each context, a preliminary investigation needs to be performed when sentiment analysis is used in a new dataset in order to guarantee a reasonable prediction performance.
Finally, although both methods perform equally well, TFBSs as predictors of operons are advantageous since additional regulatory information about a particular putative operon may be inferred.
Information generated from the use of culture and molecular methods performed in combination will likely continue to generate useful information about how the vaginal microbiota impacts human health.
All methods performed well with no errors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com