Source:
West, M., Blanchette, C., Dressman, H., Huang, E., Ishida, S., Spang, R., ... & Nevins, J. R. (2001). Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences, 98(20), 11462-11467.
Original data:
http://data.cgt.duke.edu/west.php
Description:
The authors have
developed Bayesian regression models that provide predictive capability based
on gene expression data derived from DNA microarray analysis of a series of
primary breast cancer samples. These patterns have the capacity to discriminate
breast tumors on the basis of estrogen receptor status and also on the
categorized lymph node status. Their data consisted of estrogen-receptor-positive
(ER+) and estrogen-receptor-negative (ER-) tumors.
Sample distribution: