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- T Sørlie, C M Perou, +14 authors A L Børresen-Dale
- Proceedings of the National Academy of Sciences…
- 2001

The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering.… (More)

- Trevor J. Hastie, Robert Tibshirani, Jerome H. Friedman
- Springer series in statistics
- 2009

In the words of the authors, the goal of this book was to “bring together many of the important new ideas in learning, and explain them in a statistical framework.” The authors have been quite successful in achieving this objective and their work will be a welcome addition to the statistics and learning literatures. Statistics has always been an… (More)

- Therese Sorlie, Robert Tibshirani, +13 authors David Botstein
- Proceedings of the National Academy of Sciences…
- 2003

Characteristic patterns of gene expression measured by DNA microarrays have been used to classify tumors into clinically relevant subgroups. In this study, we have refined the previously defined subtypes of breast tumors that could be distinguished by their distinct patterns of gene expression. A total of 115 malignant breast tumors were analyzed by… (More)

- Jerome Friedman, Trevor Hastie, Rob Tibshirani
- Journal of statistical software
- 2010

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multinomial regression problems while the penalties include ℓ(1) (the lasso), ℓ(2) (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent,… (More)

- Hui Zou, Trevor Hastie
- 2004

We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model… (More)

- T Hastie, R Tibshirani
- Statistical methods in medical research
- 1995

This article reviews flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints. Applications to survival models and binary outcome models are illustrated.

- Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Gilbert Chu
- Proceedings of the National Academy of Sciences…
- 2002

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… (More)

The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a… (More)

- Sandip Biswal, Trevor Hastie, Thomas P Andriacchi, Gabrielle A Bergman, Michael F Dillingham, Philipp Lang
- Arthritis and rheumatism
- 2002

OBJECTIVE
To evaluate the rate of progression of cartilage loss in the knee joint using magnetic resonance imaging (MRI) and to evaluate potential risk factors for more rapid cartilage loss.
METHODS
We evaluated baseline and followup MRIs of the knees in 43 patients (minimum time interval of 1 year, mean 1.8 years, range 52-285 weeks). Cartilage loss was… (More)

- Olga G. Troyanskaya, Michael N. Cantor, +5 authors Russ B. Altman
- Bioinformatics
- 2001

MOTIVATION
Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose… (More)