b a c k g r o u n d  & aim: it is very helpful to classify and predict the clinical  category  of a sample based  on  its  gene  expression  profile.  this  study  was  conducted  to predict  tissues  of colorectal adenoma,  adenocarcinoma,  and  paired  normal  in  colon  based  on  microarray  data  using  nearest shrunken centroid method. methods   &  materials:    in  this  study,   the  co...

The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nearest to it. In our study, the nearest shrunken centroid classifier was used simply to select important genes prior to classification. Random Forest, a decision tree based classification algorithm, is chosen as a classi...

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, ...

Nearest shrunken centroid classifier (NSC) is a class of linear classifiers with built-in feature selections, and has proven useful for analyzing microarray data. The simple linear structure of the classification boundary makes NSC easy to interpret and implement, but sometimes this simple structure might fail to generalize well for some data. In this paper we propose boosting NSC to improve it...

In this paper, we study the widely used nearest shrunken centroid classifier (NSC, also known as PAM) for microarray data from the supervised dimension reduction perspective. A simple modification is proposed and through application to public microarray data, we illustrate the favorable performance of the proposed method. Supplementary information can be found at http://www.biostat.umn. edu/~ba...

On the NCI 60 data, both Figure 1 in [1] and the revised Figure 1 showed that USC generally produces higher prediction accuracy than the ‘shrunken centroid’ algorithm (SC) [2] using the same number of relevant genes. Using the revised software implementation, USC requires fewer (2,116 instead of 2,315 as reported in [1]) genes to achieve 72% accuracy. The number of genes required by SC to achie...

Malignant pleural mesothelioma (MPM) is a rare asbestos related cancer, aggressive and unresponsive to therapies. Histological examination of pleural lesions is the gold standard of MPM diagnosis, although it is sometimes hard to discriminate the epithelioid type of MPM from benign mesothelial hyperplasia (MH).This work aims to define a new molecular tool for the differential diagnosis of MPM, ...