Source:
@ARTICLE{Shipp2002,
author = {Margaret A
Shipp and Ken N Ross and Pablo Tamayo and Andrew P Weng and Jeffery L Kutok and
Ricardo C T Aguiar and Michelle Gaasenbeek and Michael Angelo and Michael Reich
and Geraldine S Pinkus and Tane S Ray
and Margaret A Koval and Kim W Last and Andrew Norton and T. Andrew Lister and Jill Mesirov and Donna S
Neuberg and Eric S Lander and Jon C Aster and Todd R Golub},
title = {Diffuse large
B-cell lymphoma outcome prediction by gene-expression profiling and supervised
machine learning.},
journal = {Nat Med},
year = {2002},
volume = {8},
pages = {68--74},
number = {1},
month = {Jan},
doi =
{10.1038/nm0102-68},
url =
{http://dx.doi.org/10.1038/nm0102-68}
}
Original data: http://www.broad.mit.edu/mpr/lymphoma/
Description:
Among other types of
analyses, the authors investigated whether a supervised learning algorithm
could generate a classifier able to discriminate tumors within a single
(B-cell) lineage. Specifically, they asked weather the classifier could distinguish
diffuse large B-cell lymphoma (DLBCL) from a related GC B-cell lymphoma,
follicular (FL). Although these two malignancies have very different clinical
presentation, natural histories and responses to therapy, FLs often evolve over
time and acquire the morphologic and clinical features of DLBCLs.
Parameters used in our filter: