ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data.

ABSTRACT: The extraction of novel information from omics data is a challenging task, in particular, since the number of features (e.g. genes) often far exceeds the number of samples. In such a setting, conventional parameter estimation leads to ill-posed optimization problems, and regularization may be required. In addition, outliers can largely impact classification accuracy.Here we introduce ROSIE, an ensemble classification approach, which combines three sparse and robust classification methods for outlier detection and feature selection and further performs a bootstrap-based validity check. Outliers of ROSIE are determined by the rank product test using outlier rankings of all three methods, and important features are selected as features commonly selected by all methods.We apply ROSIE to RNA-Se

SUBMITTER: Jensch A 

PROVIDER: S-EPMC9014683 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature