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However, since the evolutionary model is mismatched, their performance on transposition datasets is questionable, as indicated by our experimental results shown in the next section.
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Due these reasons, the practical applicability and robustness of such methods on large sample high dimensional datasets are questionable.
Therefore, both consistency and robustness of the mechanisms unveiled by DEG Enrichment across datasets are questionable.
Unfortunately their sample size was limited, and they did not perform a validation of their signature on an independent dataset; therefore it is questionable whether they have found a robust medullary carcinoma signature or merely a classifier that defined other 'medullary like' features, such as dense lymphocytic infiltrate.
Importantly, the usefulness of the empirical dataset for evaluating modularity methods is questionable.
The SNR of some datasets generated by GNW is so low that it is questionable that they are informative for network inference (Tjärnberg et al., 2013).
That, too, is questionable.
That is questionable.
Yet that is questionable.
The other is questionable.
Historically, that is questionable.
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