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Processing and filtering the features of datasets.
(4) Processing and filtering the features of datasets Data set feature processing scheme includes feature collection, feature conversion and reservation.
After an initial hiatus (which will probably happen), we will soon come to balance the benefits of various features of datasets, including size of data, source of data, quality of data, strength of effect, plausibility of effect, reproducibility of effect, as well as issues about prespecification and multiplicity adjustments.
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The list of 41 features of dataset is shown in appendix as Table 4.
The proposed method contains some steps including Harris corner detector for feature extraction, phase correlation technique to estimate the rotation angle of head or eye movement in front of the retina fundus camera, and a new similarity function to compute the similarity between the features of dataset and test images.
The demographic and clinical features of dataset 2 subjects/patients are shown in Supplementary Table S1.
High evolutionary rates, as measured by Ka/Ks values, are the salient feature of datasets comprised of such genes [ 32, 33, 51].
A specific feature of datasets coming from genomics is the presence of a very large number of measurements concerning gene expressions but only a relatively small number of observations.
Also, as another result, due to low complexity, we can use a smaller dataset which is one of the main features of petroleum datasets (Feldman and Ballard 1982; Jacobs et al. 1991; Jacobs 1995).
We started with basic syntax and briefly discussed how to explore the features of our datasets.
CoMFA steric and electrostatic, CoMSIA hydrophobic and hydrogen bond acceptor fields, and HQSAR atomic contribution maps were used to analyze the structural features of the datasets that govern their antagonistic potency.
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