Transcription profiling of acute lymphoblastic leukaemia patient samples that represent six different subgroups defined by cytogenetic features and immunophenotype

ABSTRACT: We examined published microarray data from 104 acute lymphoblastic leukaemia patient specimens, that represent six different subgroups defined by cytogenetic features and immunophenotypes. Using the decision-tree based supervised learning algorithm Random Forest (RF), we determined a small set of genes for optimal subgroup distinction and subsequently validated their predictive power in an independent cohort of 68 specimens that were assessed using Affymetrix HG-U133A arrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Katrin Hoffmann 

PROVIDER: E-TABM-125 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress