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As we know, data testing in the laboratory is more accurate than calculation using a food composition table and questionnaire.
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We also noticed that the Bismark pipeline performed best in our real data test in terms of the accuracy of SNP calling (Additional file 1: Table S5).
However, we found some multi-level observations in both simulation and real data testing (see in additional file 1).
Test data in Set A are matched to the multi-condition train data, test data in Set B are not matched to the multi-condition train data, and test data in Set C are further mismatched due to convolution.
The largest bottleneck in big data testing is verifying that the data has been successfully processed.
Integrated data were tested in distribution and logical accuracy by working group for data clean up.
Two methods for temperature correction of electrical resistivity data were tested in this study — both gave similar results.
To increase reliability of the collected heterogeneous data we tested in silico how candidate genes interact at the proteomic level.
The accuracy of these data was tested in cases through direct questioning of patients and relatives and review of records.
However these data now warrant testing in other larger cohorts and other refinements.
Finally, we classified each data in testing dataset according to the trained SVM classifier.
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CEO of Professional Science Editing for Scientists @ prosciediting.com