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A number of previous studies have also investigated the level of consensus found between different experimental datasets.
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Generating new therapeutic hypotheses for human disease requires the analysis and interpretation of many different experimental datasets.
The degree of overlap between the three datasets can be seen in Figure 4A (only 2 genes are shared by all three sets) highlight the variability of gene expression across the different experimental datasets.
Based on comparison with different experimental datasets, the predicted causal genes are clearly statistically significant.
The results from each are then compared to the three different experimental datasets available in literature.
This feature allows comparison and the recurrence analysis of the fusion candidates within both the same experimental dataset (samples of the same disease) and within different experimental datasets (samples across different diseases).
This makes between-sample normalization critical regarding comparisons of gene expression between different experimental conditions, as our results illustrate.
Figure 5 shows both UniFrac distances between different experimental procedures.
P-values <0.05 between different experimental groups were considered significant.
Seven different experimental aerial photogrammetric datasets are used to demonstrate the efficiency and validity of the proposed algorithm.
Lampreys were randomly distributed between the different experimental groups.
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