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There are several brain circuits involved in action selection, the most relevant being the prefrontal cortex, the pre-supplementary motor area (preSMA), the supplementary motor area (SMA) and the primary motor cortex (M1).
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Thus, dimensionality reduction was achieved by feature selection approach, selecting the most relevant feature vectors for each protein.
SPSS (ver.16, http://www.spss.com) was used in variable selection step to select the most relevant descriptors.
The feature selection stage allows an effective selection of the most relevant features.
Feature selection – in our case selecting the most relevant genes for segregant response to an SMP – was performed to reduce noise in the data and hopefully make any pattern more readily identifiable.
A selection of the most relevant full respondent statements, selected from 1,769 statements (translated to English, and slightly edited when required) is reported in the text.
Important issues are treated, such as the selection of the most relevant data, the modeling technique, and variable-selection procedure.
Selection of the most relevant genes to develop prediction models for survival risk presents additional challenges.
Very few studies report on the use of an iterative variable selection procedure [ 25] to select the most relevant variables and optimize the prediction accuracy of the RFM, although the classification accuracy improved when such a protocol was applied [ 24, 25, 68, 98] (Supplementary Table S1).
Selection of the most relevant input variables for an inferential predictor is important for good prediction ability.
The findings lead to a testable mechanistic framework of how the midbrain and forebrain networks interact to control spatial attention.Attention involves the selection of the most relevant information for differential sensory processing and decision making.
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CEO of Professional Science Editing for Scientists @ prosciediting.com