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In the analysis of fMRI data, the features that are descriptive of the objects are first chosen, whereafter a subset of these features to be used for classification is selected.
This issue is now being recognised in molecular immunology [ 3], and new methods are required to be developed, in order to understand, based on high-throughput data, the features of cells from mutants in the context of well-characterised differentiation programmes.
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On the test data, the feature extraction and classification steps were executed when the signal envelope exceeded a predefined threshold.
Using a classifier trained on annotated training data, the feature vectors extracted from the songs are assigned a genre.
By closely following the shape and density distribution of the data, the feature vectors reduce the docking to a point cloud matching problem.
In order to avoid bias due to specific pre-processing of the raw data, the feature-level extraction output (FLEO) files (CEL files) are used.
The big data have the features of forecast warning and protection.
Numerical studies and comparisons to experimental data reveal the features of the numerical model for SIC.
The data for the features CAI.diff, GRAVY.diff, AROMA.diff, MW.ratio and aa were downloaded from SGD [ 30].
This study provides useful epidemiological data on the features of the spread of HEV among families as well.
For the analysis on the selected data, the main features of the lactation curves were extracted.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com