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Robert Tibshirani
Researcher at Stanford University
Publications - 620
Citations - 359457
Robert Tibshirani is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Gene expression profiling. The author has an hindex of 147, co-authored 593 publications receiving 326580 citations. Previous affiliations of Robert Tibshirani include University of Toronto & University of California.
Papers
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Journal ArticleDOI
Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment.
Ash A. Alizadeh,Andrew J. Gentles,Alvaro J. Alencar,Chih Long Liu,Holbrook E Kohrt,Roch Houot,Matthew J. Goldstein,Shuchun Zhao,Yasodha Natkunam,Ranjana H. Advani,Randy D. Gascoyne,Javier Briones,Robert Tibshirani,June Helen Myklebust,Sylvia K. Plevritis,Izidore S. Lossos,Ronald Levy +16 more
TL;DR: It is concluded that the measurement of a single gene expressed by tumor cells (LMO2) and a single genes expressed by the immune microenvironment (TNFRSF9) powerfully predicts overall survival in patients with DLBCL.
Journal ArticleDOI
Molecular assessment of surgical-resection margins of gastric cancer by mass-spectrometric imaging.
Livia S. Eberlin,Robert Tibshirani,Jialing Zhang,Jialing Zhang,Teri A. Longacre,Gerald J. Berry,David B. Bingham,Jeffrey A. Norton,Richard N. Zare,George A. Poultsides +9 more
TL;DR: Desorption electrospray ionization mass spectrometric imaging (DESI-MSI) and the statistical method of least absolute shrinkage and selection operator (Lasso) are used to classify tissue as cancer or normal based on molecular information obtained from tissue and also to select those mass-spectra features most indicative of disease state.
Journal ArticleDOI
Forward Stagewise Regression and the Monotone Lasso
TL;DR: In this paper, the authors consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems, and show that the latter is a monotone version of the lasso.
Book ChapterDOI
Overview of Supervised Learning
TL;DR: The first three examples described in Chapter 1 have several components in common, for each there is a set of variables that might be denoted as inputs, which are measured or preset.
Journal ArticleDOI
Prototype selection for interpretable classification
Jacob Bien,Robert Tibshirani +1 more
TL;DR: This paper discusses a method for selecting prototypes in the classification setting (in which the samples fall into known discrete categories), and demonstrates the interpretative value of producing prototypes on the well-known USPS ZIP code digits data set and shows that as a classifier it performs reasonably well.