Identifying subgroup markers in heterogeneous populations.

ABSTRACT: Traditional methods that aim to identify biomarkers that distinguish between two groups, like Significance Analysis of Microarrays or the t-test, perform optimally when such biomarkers show homogeneous behavior within each group and differential behavior between the groups. However, in many applications, this is not the case. Instead, a subgroup of samples in one group shows differential behavior with respect to all other samples. To successfully detect markers showing such imbalanced patterns of differential signal, a different approach is required. We propose a novel method, specifically designed for the Detection of Imbalanced Differential Signal (DIDS). We use an artificial dataset and a human breast cancer dataset to measure its performance and compare it with three traditional method

SUBMITTER: de Ronde JJ 

PROVIDER: S-EPMC3834840 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature