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The SVR model is further applied to the data that have gone through preprocessing measures (clustering, data reduction, and transformation).
In practice, proper preprocessing measures, such as antenna demodulation, noncoherent integration of multiple pulses, multichannel calibration and time delay compensation, and so forth, should be employed to enhance the quality of received signal [9, 10].
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For the PG generation algorithms, we assume that we are given the following input variables (for a network of n genes): a 1 × n row vector G wt containing the (possibly preprocessed) wild-type gene expression data the n × n matrix G ko containing the (possibly preprocessed) measured steady-state gene expression levels after perturbing/knocking-out each single gene.
This approach can be considered as an important preprocessing step to learn how the measures capture structural information of networks.
After implementing the preprocessing step of FILTERSIM algorithm, it is required to utilize the appropriate similarity criteria between the patterns; the most common similarity measure of standard representation is "distance" (Perner 2007).
(b) Retina's image after preprocessing step.
All quality control measures, preprocessing and analyses were performed using the statistical computing language R [ 28].
Dates from the second collection were preprocessed and measured by Metabolon Inc., USA alone.
Data from different GSEs or different GPLs were heterogeneous regarding of their measuring technologies, measured genes, and preprocessing methods, making it meaningless to analysis them together directly.
Therefore, the experimental studies reveal that the overall performance of the multibiometric system is improved, when multi-normalization-based score preprocessing technique and ancillary quality measures are employed on a unified framework for finding the optimal integration weights.
Standard preprocessing and registration was applied (Supplementary Material).
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