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In Gómez et al. [85], a model comparison was also done to show the relevance of each feature in every dataset.
In every dataset examined, the distribution of B-SIFT scores is shifted towards the negative end (Fig. 4A).
Thus, it may be unproductive for an investigator to invest sufficient time for this in every dataset, and for all aspects of experimental design.
In every dataset, positive and negative subsets are defined through filters.
Overall, we noted that MCODE produced less modules than CFinder in every dataset which was considered.
We independently validated the normalization in every dataset by inspecting the distribution of expression values.
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While most high-throughput experiments are designed with fairly specific goals in mind, almost every dataset contains information about a variety of biological processes, and our analysis provides several ways of exploring these data.
The smaller means and standard deviations for every dataset in comparison with the other methods show the accuracy and robustness of the new search engine as compared to the state-of-the-art search algorithms.
To assist with the use of these datasets we further provide an information file for each run containing the characteristics of every dataset in the Pareto-optimal front.
Unfortunately, Bicat implementations of xMotifs and Bimax approaches did not work properly for every dataset in Table 3. Specifically, xMotifs could not be performed for Yeast and Steminal datasets, due to unexpected runtime errors.
The fact that claudin-1 was not included in every prognosis dataset we analyzed indicates that the prognostic ability of the 39-gene signature is not reliant upon on any singular gene expression change, but rather likely represents the conglomerate result of systematic changes resulting from LPA-induced changes in RNA levels.
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CEO of Professional Science Editing for Scientists @ prosciediting.com