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However, these feature selections are with less consideration of the correlation between two feature types.
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Similarly, we compared three feature types between two ancestral populations from 1000 Genomes Project.
The feature extractors use one out of three feature types.
Commonly used local distances, such as Euclidean or Mahalanobis distances, compute the difference between two feature vectors directly [10], and they are thus of a feature-feature type.
While many previous studies generally used mutual information to detect dependency between two features of the same type, more recent approaches in [ 12, 13] have also extended the mutual information to assess the association between expressions of two genomic features, X1 and X2, and the clinical outcome Y as follows: I (X 1, X 2 ; Y ) = H (X 1, X 2 ) + H (Y ) - H (X 1, X 2, Y ).
We use a 'genome-build' header that provides the genome build against which the GFF3 was generated, and we add special handling for a 'parental_relationship' attribute that specifies the type of relationship between two features linked using the existing Parent attribute.
An observed correlation between two features that is caused by direct relationships between these two features and a third feature.
Classification was performed using two distinct feature types: histone modification features and DNA sequence features.
We present a solution for the evaluation of untargeted metabolomic method performance that enables us to distinguish between two types of features: artifactual features and biologically derived features.
These methods ignore the relation between different feature types.
The designer can choose between two possible types of training feature space after the appropriate feature selection.
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