Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.

ABSTRACT:

Background

The inclusion of high-dimensional omics data in prediction models has become a well-studied topic in the last decades. Although most of these methods do not account for possibly different types of variables in the set of covariates available in the same dataset, there are many such scenarios where the variables can be structured in blocks of different types, e.g., clinical, transcriptomic, and methylation data. To date, there exist a few computationally intensive approaches that make use of block structures of this kind.

Results

In this paper we present priority-Lasso, an intuitive and practical analysis strategy for building prediction models based on Lasso that takes such block structures into account. It requires the definition of a priority order of blocks of

SUBMITTER: Klau S 

PROVIDER: S-EPMC6134797 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature