Source:

@ARTICLE{Golub1999,

author = {T. R. Golub and D. K. Slonim and P. Tamayo and C. Huard and M. Gaasenbeek and J. P. Mesirov and H. Coller and M. L. Loh and J. R. Downing and M. A. Caligiuri and C. D. Bloomfield and E. S. Lander},

title = {Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.},

journal = {Science},

year = {1999},

volume = {286},

pages = {531--537},

number = {5439},

month = {Oct},

}

Original data: http://www-genome.wi.mit.edu/cgi-bin/cancer/datasets.cgi

Description:

The authors present a class discovery procedure to automatically discover the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. They also showed that the procedure can further categorize distinguish between B-cell and T-cell ALL. They use for training their model a dataset with 38 samples and another dataset with 34 samples for testing. In our case, we merge these two data sets and from them build the following:

• Golub-V1: 47 ALL and 25 AML
• Golub-V2: 38 ALL-B, 9 ALL-T and 25 AML.

Parameters used in our filter:

• Golub-V1 and Golub-V2: l=2 and c=18