#### Filter Results:

- Full text PDF available (47)

#### Publication Year

1962

2017

- This year (5)
- Last 5 years (41)
- Last 10 years (68)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

#### Method

Learn More

- Martin Kircher, Daniela M Witten, Preti Jain, Brian J O'Roak, Gregory M Cooper, Jay Shendure
- Nature genetics
- 2014

Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure… (More)

- Daniela M Witten, Robert Tibshirani, Trevor Hastie
- Biostatistics
- 2009

We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as circumflexX = sigma(k=1)(K) d(k)u(k)v(k)(T), where d(k), u(k), and v(k) minimize the squared Frobenius norm of X - circumflexX, subject to penalties on u(k) and v(k). This results in a regularized version of… (More)

- Patrick Danaher, Pei Wang, Daniela M Witten
- Journal of the Royal Statistical Society. Series…
- 2014

We consider the problem of estimating multiple related Gaussian graphical models from a high-dimensional data set with observations belonging to distinct classes. We propose the joint graphical lasso, which borrows strength across the classes in order to estimate multiple graphical models that share certain characteristics, such as the locations or weights… (More)

- Line H. Clemmensen, Trevor J. Hastie, Daniela Witten, Bjarne K. Ersbøll
- Technometrics
- 2011

Classication in high-dimensional feature spaces where interpretation and dimension reduction are of great importance is common in biological and medical applications. For these applications standard methods as microarrays, 1D NMR, and spectroscopy have become everyday tools for measuring thousands of features in samples of interest. Furthermore, the samples… (More)

- Daniela M Witten, Robert J Tibshirani
- Statistical applications in genetics and…
- 2009

In recent work, several authors have introduced methods for sparse canonical correlation analysis (sparse CCA). Suppose that two sets of measurements are available on the same set of observations. Sparse CCA is a method for identifying sparse linear combinations of the two sets of variables that are highly correlated with each other. It has been shown to be… (More)

- Jun Li, Daniela M Witten, Iain M Johnstone, Robert Tibshirani
- Biostatistics
- 2012

We discuss the identification of genes that are associated with an outcome in RNA sequencing and other sequence-based comparative genomic experiments. RNA-sequencing data take the form of counts, so models based on the Gaussian distribution are unsuitable. Moreover, normalization is challenging because different sequencing experiments may generate quite… (More)

- Daniela M Witten, Robert Tibshirani
- Journal of the Royal Statistical Society. Series…
- 2011

We consider the supervised classification setting, in which the data consist of p features measured on n observations, each of which belongs to one of K classes. Linear discriminant analysis (LDA) is a classical method for this problem. However, in the high-dimensional setting where p ≫ n, LDA is not appropriate for two reasons. First, the standard estimate… (More)

- Tim C P Somervaille, Christina J Matheny, +7 authors Michael L Cleary
- Cell stem cell
- 2009

The genetic programs that promote retention of self-renewing leukemia stem cells (LSCs) at the apex of cellular hierarchies in acute myeloid leukemia (AML) are not known. In a mouse model of human AML, LSCs exhibit variable frequencies that correlate with the initiating MLL oncogene and are maintained in a self-renewing state by a transcriptional subprogram… (More)

- Daniela M Witten, Robert Tibshirani
- Journal of the American Statistical Association
- 2010

We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse… (More)

- Daniela M Witten, Robert Tibshirani
- Journal of the Royal Statistical Society. Series…
- 2009

In recent years, many methods have been developed for regression in high-dimensional settings. We propose covariance-regularized regression, a family of methods that use a shrunken estimate of the inverse covariance matrix of the features in order to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing its log… (More)