A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
|Author||Osama Mahmoud, Andrew Harrison, Aris Perperoglou, Asma Gul, Zardad Khan, Berthold Lausen|
|Date of publication||2014-09-15 17:06:03|
|Maintainer||Osama Mahmoud <firstname.lastname@example.org>|
|License||GPL (>= 2)|
CoreIntervals: Computing the Core Intervals for Both Classes.
GMask: Producing Gene Masks.
leukaemia: Leukaemia data set.
lung: Lung cancer data set.
POS: Calculating the proportional Overlapping Scores.
propOverlap-internals: Internal 'propOverlap' functions.
propOverlap-package: Feature (gene) selection based on the Proportional...
RDC: Assiging the Relative Dominant Class.
Sel.Features: Gene (Feature) Selection.