It means the problem is theirs dataset were not enough for creating their behavior model.
It can be seen that in the current dataset there is not enough data to explore the extent to which similarities in transporter sequences determine similarities in compounds transported, since having a more complete data matrix would be required a selection of compounds tested against a selection of transporters in an all-to-all fashion would be ideal.
Although useful, the coverage of this dataset is not comprehensive enough for kinase statistical enrichment analysis.
While this dataset is not large enough to derive statistically sound conclusions, many general trends were noted.
Moreover, the statistical power of this methodology may be significantly reduced when the training dataset is not big enough.
Secondly, there may be situations where a validation set is not available (typically because the available dataset is not large enough to be split).
Additionally, although comorbid illnesses were measured in this study and included as covariates in our model, the size of our dataset is not large enough to allow for us to weight individual illnesses for their impact upon physical activity.
Inferences regarding introduction scenarios are only as robust as the sampling of the potential source populations (Dlugosch and Parker 2008), however, and our dataset is not extensive enough to rule out that unsampled source populations rather than admixture of our observed native populations have produced the current invasions.
While this dataset is not large enough for well-powered multivariable analyses, the HR comparing Active to Inactive remained stable and above 2.0 even after adjusting for grade (high and medium versus low as two indicator variables), subtype (normal-like, luminal B, HER2 and basal-like versus luminal A as four indicator variables), size (< 2 cm versus >/ = 2 cm) and age (continuous).
Critical opinions such as voiced in several media interviews by Allan Frances, the chairman of the committee that was responsible for the DSM-IV, indicate that a major revision of DSM-IV is not needed at all, is a waste of money, energy and existing datasets; this simply because there is not enough new and sufficient evidence to reorganize the current classification system.
