Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu
Diagnosis of multiple cancer types by shrunken centroids of gene expression
PNAS May 14, 2002 vol. 99 no. 10 6567-6572
[PDF][Web Site]
・Shurunken centroids を利用したクラス分け法の提案。
・データ
1.SRBCT, 63 training/25 test samples, 2308 genes [Khan]
2.Leukemia, 20 ALL/14 AML samples, 7129 genes [Golub]
・概要「We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier.」
・「Our method of "nearest shrunken centroids" identifies subsets of genes that best characterize each class.」
・「we propose a simple modification of the nearest-centriod, called "nearest shrunken centroid." This approach uses "de-noised" versions of the centroids as prototypes for each class.」
・問題点「The problem of classification by microarrays is challenging because:
・there is a large number of classification by microarrays is challenging because:
・there are a large number of inputs (genes) from which to predict classes and a relatively small number of samples, and
・it is important to identify which genes contribute most to the classification.」
・目的「One goal of our method is to find the smallest set of genes that can accurately classify samples.」
・従来の nearest centroid よりも条件を厳しくして遺伝子を厳選する、ということらしいが、方法のキモ(どういう基準で?)がいまいちつかめず。論文をさかのぼらないとダメか。
Diagnosis of multiple cancer types by shrunken centroids of gene expression
PNAS May 14, 2002 vol. 99 no. 10 6567-6572
[PDF][Web Site]
・Shurunken centroids を利用したクラス分け法の提案。
・データ
1.SRBCT, 63 training/25 test samples, 2308 genes [Khan]
2.Leukemia, 20 ALL/14 AML samples, 7129 genes [Golub]
・概要「We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier.」
・「Our method of "nearest shrunken centroids" identifies subsets of genes that best characterize each class.」
・「we propose a simple modification of the nearest-centriod, called "nearest shrunken centroid." This approach uses "de-noised" versions of the centroids as prototypes for each class.」
・問題点「The problem of classification by microarrays is challenging because:
・there is a large number of classification by microarrays is challenging because:
・there are a large number of inputs (genes) from which to predict classes and a relatively small number of samples, and
・it is important to identify which genes contribute most to the classification.」
・目的「One goal of our method is to find the smallest set of genes that can accurately classify samples.」
・従来の nearest centroid よりも条件を厳しくして遺伝子を厳選する、ということらしいが、方法のキモ(どういう基準で?)がいまいちつかめず。論文をさかのぼらないとダメか。