The data set includes gene expression information on 110 childhood acute lymphoblastic leukemia samples. For this data set we induced models for two different classification problems. With the second model we try to distinguish between childhood acute lymphoblastic leukemia cells based on changes in gene expression after four different treatment types.
Platform: Affymetrix GeneChip Human Genome U95 Version [1 or 2] Set HG-U95A
- mercaptopurine and low-dose methotrexate (LDMTX_MP): 16 examples (26.7%)
- mercaptopurine and high-dose methotrexate (HDMTX_MP): 10 examples (16.7%)
Number of genes: 8280 Number of samples: 60 Note: From the originally measured 12625 probe sets we removed genes that were not present (P) in at least one sample
Predictive accuracy with 10-fold cross validation (classifying using the best projection with eight attributes):
Following are the three best-ranked visualization with eight, six and four attributes in respect to the visualization score, that is, visualizations where examples from different diagnostic classes are best separated: