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In addition, there are likely to be data quality issues in the routine HES dataset, in particular in relation to diagnosis coding, which may be inconsistent and not provide full details on likely causes of readmission.
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However, any such adaptation is likely to be data-dependent.
Indeed, unlike scientific applications in other scientific domains, applications in life sciences are likely to be data-intensive and need to be implemented as pipelines, which makes HPC environments somewhat unfit.
As the UDI data are likely to be skewed, data will be log transformed prior to analysis.
The end result is not likely to be perfect data quality, but the ambition should be to move toward data credibility.
Where the prevalence of poor-quality antimalarials is likely to be high, data are inherently scarce.
First, there is likely to be missing data that will likely increase with time.
Respondents with both diabetes and cancer were more likely to be missing data on depression.
Respondents with cancer were more likely to be missing data on physical activity, although the differences were small.
When classifying as young or old, the 5-day insects were most likely to be misclassified (data not shown).
We thus consider the results from qRT-PCR and the Illumina platform more likely to be "true data" in the present study.
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