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In most prior research, we find that the indexes of each data are stored by a row, not by a field (column) as another inefficient respect.
Each data are represented by a number of triangle areas as its feature vectors, in which a triangle area is based on the data, its cluster center, and one of the other clusters.
As expected, the replicates in each data are highly correlated, and the Pearson correlation coefficients between the replicates in X1 or X2 are greater than 0.95.
The algorithms and models used to address subchallenge 1 include a bidirectional search algorithm to select a set of features from each data are set where the training and testing is performed with a supervised learning method, SVM with a radial basis kernel.
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Each data was represented as mean ± SD (n = 3).
Each data was averaged from at least 5 independent calli.
Each data was the average of 100 observations.
Each data is recorded according to the travel distance and data recording interval is 10 cm.
Also, the mean square error of each data is given in Table 1.
Each data was then reported to β-actin density.
Each data was normalized by the expression level of peptidylprolyl isomerase A (Ppia), an internal control.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com