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It is important that both data sets have the same statistical properties and SOM were used for this purpose.
Both data sets have immunohistochemistry data of estrogen receptor (ER) status, which is known to play important role in breast cancer biology, e.g., BRCA1 mutation, lymphocytic infiltration, and early occurrence of distant metastasis [11], [13].
To increase specificity, only interactions confirmed in both data sets have been retained.
Therefore, in this communication both data sets have been combined and analyzed uniformly.
The test computes the difference between the likelihood scores obtained when both data sets have different topologies ('two trees model') and the likelihood scores obtained when both data sets have the same topology ('one tree model').
Both data sets have been validated against the medical records in separate studies at different times [ 22, 23].
Similar(52)
This result is interesting since both data sets had a similar proportion of events occurring among ER positive and negative tumors and ER status was associated with outcome in both data sets.
Fourteen of the 129 SNP with extreme FST in both data sets had an effect on one of the three traits (RFI, yield and IMF), six of which had an effect on more than one trait (Table 3).
The histograms and summary statistics of the two model data sets are highly comparable with their corresponding domain data sets, except that both domain data sets have more extreme values because of larger sample sizes.
Both data-sets had the same RMSE in the frontal view (0°), because the frontal view was not affected by self-occlusion.
Extensive experiments with both real and synthetic data sets have been conducted to verify the efficiency and effectiveness of our proposed approach compared to the state-of-the-art approaches.
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