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Moreover, successful applications in big medical datasets are expected to dramatically scaling up MCMAR for complex infant brain MRI in terms of efficiency and feasibility.
On future exascale platforms, the datasets are expected to be too large to be efficiently stored and transferred across networks to analysis and visualization workstations, thus impeding data exploration and knowledge discovery.
Moreover, a small percentage of the compounds in these putative non-inhibitor datasets are expected to be un-reported and un-discovered inhibitors, their presence in these datasets is not expected to significantly affect the estimated false hit rate of SVM.
Phylogenetic analyses of nuclear versus mitochondrial datasets are expected to come to similar conclusions, thus providing independent evidence for the given phylogenetic relationships.
Given that datasets are expected to increase over time, Hadoop is a framework that we envision will have an increasingly important role in future biological data analysis.
In addition to the alternate genotype codings and covariates listed above, tests of heterogeneity between datasets are expected to lead to secondary meta-analyses of results from more homogeneous datasets.
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Such incompleteness in crawled datasets is expected due to intrinsic restrictions of web crawling such as rate limit and privacy protection.
Small differences detected among the 5 GEO Datasets were expected to occur due to divergence in the methodology used in the experimental procedures and in the analysis of each gene.
Using multiple datasets was expected to remedy this effect.
The richer information contained in the multilevel datasets is expected to improve the multiple imputations.
A combination of the analyses from both datasets is expected to reduce the possibility of data misinterpretation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com