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The Methods section begins with a description of the dataset preparation in Section 1 titled "Selection and Preparation of Datasets…".
Some of the dataset preparation, running of experiments and post processing of results were accelerated by PAR [32].
The different tests also highlighted the importance of the dataset preparation, by cutting complex feature into sub-features with a unique orientation.
To generate more realistic results, we have also evaluated the three techniques including the dataset preparation steps presented in Section 3.4.
The first phase focussed on the dataset preparation and preparatory analyses.
It has been tested and fine-tuned for several years in our laboratory and its use leads to significant time savings at the dataset preparation and analysis stages.
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The adoption of these dataset preparation steps argues for a strong construct validity in our study.
A further challenge was the burden dataset preparation placed on the MRC/Wits-Agincourt staff.
BZ carried out the GIS dataset preparation, and helped to draft the manuscript.
We used a set of 470 human microRNAs obtained from miRBase release 9.1 [34], [34] and performed PITA predictions for the 3'UTRs of the 40 upregulated and 1200 nonregulated mRNAs described in the eIF4E dataset preparation section.
For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure.
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