Mike West, Carrie Blanchette, Holly Dressman, Erich Huang, Seiichi Ishida, Rainer Spang, Harry Zuzan, John A. Olson Jr., Jeffrey R. Marks, and Joseph R. Nevins
Predicting the clinical status of human breast cancer by using gene expression profiles
PNAS September 25, 2001 vol. 98 no. 20 11462-11467
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・遺伝子発現データに基づく乳ガンの診断
・データ:Breast cancer, Duke Breast Cancer SPORE frozen tissue bank より提供された組織, Affy
・問題点「
Traditional methods of phenotypic characterization are often limited and do not have the ability to discern subtle differences that may be of importance for developing a better understanding of the tumor and advancing therapeutic strategies for the treatment of disease.」
・「
The analysis of gene expression represents an indirect measure of the genetic alterations in tumors because, in most instances, these alterations affect gene regulatory pathways.」
・解析法「
Analysis uses binary regression models combined with singular value decompositions (SVDs) and with stochastic regularization by using Baysian analysis」
・「
We note that, in some applied contexts, the levels of extraneous noise may be lower than in the complex and challenging case of breast cancer;」
・処理「
The binary regression model was then fitted to the set of 100 selected genes by using the resulting SVD factors on the basis of these 100 genes.」
・問題点「
ER status is simply difficult to determine, because of either within-tumor heterogeneity or changes over time in protein levels.」
・内容が読み取りずらい。解析法よりもデータがメイン。