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A few more details on this data preparation procedure are described below.
We use the data preparation procedure described in [ 22].
At the end of the data preparation procedure, SNPTracker reads the SNPChrPosOnRef dataset to retrieve coordinates for all input variants.
For the prediction of external data, such a separate data preparation procedure may be suitable in some situations, for example when the external data behaves very differently from the training data: by a separate processing the data preparation procedure can adjust itself to the peculiarities of the external data; see e.g. [ 16].
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All the data preparation procedures are performed with in-house Python scripts.
Using Baltimore City as the study area, this case study aims to illustrate the data preparation procedures of the proposed concurrent station location and alignment optimization model, and to demonstrate its effectiveness compared to the sequential optimization methodology where stations are first selected and alignment is then designed between these selected stations.
Standard data preparation and enhancement procedures including refraction statics correction, noise attenuation, deconvolution and common midpoint sorting (CMP) were carried out to improve data signal-to-noise ratio.
Thus, our data clearly demonstrated that the preparation procedure had no obvious effect on the MMP-1 enzymatic activity.
Some preparation tasks for exploring effective optimization method in power distribution systems are conducted, including problem definition, procedure clarification and data preparation.
The data mining procedures used for data preparation, modeling and simulation, which include statistics, methods of artificial intelligence and hybrid models in form of cascade committee machines, are described.
This paper proposes a step-by-step procedure, which deals with data preparation, practical issues associated with fault data sets, and implementation of association rule mining.
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