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We built these data structures using the Hadoop distributed computing platform with Map-Reduce [9].
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Google and IBM and Microsoft spend lots of money when they build these data centers out away from big population centers.
Oracle, IBM and companies like Teradata build these data warehouse systems that cost millions to develop and months to implement.
Models are built with these data, and are held together by the glue of laws or observed relationships in the absence of a law.
A lot of the bandwidth and capacity has to be built into these data centers.
Predictions for conditions inside the red line can be expect to be good from a model built on these data.
We built on these data to analyse experimentally the cis-acting mechanisms of FSCN1 transcriptional regulation in human colon and breast carcinoma cells.
A Random Forest was built with these data, which resulted in an accuracy of 81.0%.
The consensus linkage map built with these data covers 136.2 cM with a mean inter-locus distance of 5 cM.
A five-factor model was also built with these data in order to have a model with the same number of factors than the original theoretical model described in the bibliography.
PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com