D
Daniela Witten
Researcher at University of Washington
Publications - 149
Citations - 32016
Daniela Witten is an academic researcher from University of Washington. The author has contributed to research in topics: Lasso (statistics) & Graphical model. The author has an hindex of 47, co-authored 140 publications receiving 24957 citations. Previous affiliations of Daniela Witten include Institute for Advanced Study & Stanford University.
Papers
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BookDOI
An introduction to statistical learning
TL;DR: An introduction to statistical learning provides an accessible overview of the essential toolset for making sense of the vast and complex data sets that have emerged in science, industry, and other sectors in the past twenty years.
Journal ArticleDOI
A general framework for estimating the relative pathogenicity of human genetic variants
Martin Kircher,Daniela Witten,Preti Jain,Brian J. O'Roak,Brian J. O'Roak,Gregory M. Cooper,Jay Shendure +6 more
TL;DR: The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.
Book
An Introduction to Statistical Learning: with Applications in R
TL;DR: This book presents some of the most important modeling and prediction techniques, along with relevant applications, that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
Journal ArticleDOI
CADD: predicting the deleteriousness of variants throughout the human genome.
Philipp Rentzsch,Daniela Witten,Gregory M. Cooper,Jay Shendure,Martin Kircher,Martin Kircher +5 more
TL;DR: The latest updates to CADD are reviewed, including the most recent version, 1.4, which supports the human genome build GRCh38, and also present updates to the website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications.
Journal ArticleDOI
A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis
TL;DR: A penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix, and establishes connections between the SCoTLASS method for sparse principal component analysis and the method of Zou and others (2006).