Subtype prediction in pediatric acute myeloid leukemia: classification using differential network rank conservation revisited.

ABSTRACT:

Background

One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family's simple decision rules with known biological relationships, this systems approach identifies interpretable

SUBMITTER: Obulkasim A 

PROVIDER: S-EPMC4580220 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature