Nearest shrunken centroids via alternative genewise shrinkages
نویسندگان
چکیده
منابع مشابه
Nearest shrunken centroids via alternative genewise shrinkages
Nearest shrunken centroids (NSC) is a popular classification method for microarray data. NSC calculates centroids for each class and "shrinks" the centroids toward 0 using soft thresholding. Future observations are then assigned to the class with the minimum distance between the observation and the (shrunken) centroid. Under certain conditions the soft shrinkage used by NSC is equivalent to a L...
متن کاملImproved centroids estimation for the nearest shrunken centroid classifier
MOTIVATION The nearest shrunken centroid (NSC) method has been successfully applied in many DNA-microarray classification problems. The NSC uses 'shrunken' centroids as prototypes for each class and identifies subsets of genes that best characterize each class. Classification is then made to the nearest (shrunken) centroid. The NSC is very easy to implement and very easy to interpret, however, ...
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There are various types of classifiers that can be trained on gene expression data with class labels. Many of them have an embedded mechanism for feature selection, by which they distinguish a subset of significant genes that are used for future prediction. When dealing with more than two class labels, especially when the number goes up to a dozen or more, people find it useful to know the rela...
متن کاملContext Aware Group Nearest Shrunken Centroids in Large-Scale Genomic Studies
Abstract Recent genomic studies have identified genes related to specific phenotypes. In addition to marginal association analysis for individual genes, analyzing gene pathways (functionally related sets of genes) may yield additional valuable insights. We have devised an approach to phenotype classification from gene expression profiling. Our method named “group Nearest Shrunken Centroids (gNS...
متن کاملClass prediction by nearest shrunken centroids,with applications to DNA microarrays
We propose a new method for class prediction in DNA microarray studies, based on an enhancement of the nearest prototype classi er. Our technique uses \shrunken" centroids as prototypes for each class and identi es the subsets of the genes that best characterize each class. The method is general, and can be used in other high-dimensional classi cation problems. The method is illustrated on data...
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ژورنال
عنوان ژورنال: PLOS ONE
سال: 2017
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0171068