Diagnosis of multiple cancer types by shrunken centroids of gene expression.
We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier. We shrink the prototypes and hence obtain a classifier that is often more accurate than competing methods. Our method of "nearest shrunken centroids" identifies subsets of genes that best characterize each class. The technique is general and can be used in many other classification problems. To demonstrate its effectiveness, we show that the method was highly efficient in finding genes for classifying small round blue cell tumors and leukemias.
Tibshirani
Robert
R
Department of Health, Research and Policy, and Statistics, Stanford University, Stanford, CA 94305, USA. tibs@stat.stanford.edu
Hastie
Trevor
T
Narasimhan
Balasubramanian
B
Chu
Gilbert
G
D002648:Child; D004273:DNA, Neoplasm / Q000032:analysis*; D016002:Discriminant Analysis; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D011336:Probability
D004273:DNA, Neoplasm