Block HSIC Lasso: model-free biomarker detection for ultra-high dimensional data.

ABSTRACT:

Motivation

Finding non-linear relationships between biomolecules and a biological outcome is computationally expensive and statistically challenging. Existing methods have important drawbacks, including among others lack of parsimony, non-convexity and computational overhead. Here we propose block HSIC Lasso, a non-linear feature selector that does not present the previous drawbacks.

Results

We compare block HSIC Lasso to other state-of-the-art feature selection techniques in both synthetic and real data, including experiments over three common types of genomic data: gene-expression microarrays, single-cell RNA sequencing and genome-wide association studies. In all cases, we observe that features selected by block HSIC Lasso retain more information about the underlying biolo

SUBMITTER: Climente-Gonzalez H 

PROVIDER: S-EPMC6612810 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature