Benchmark study of feature selection strategies for multi-omics data.

ABSTRACT:

Background

In the last few years, multi-omics data, that is, datasets containing different types of high-dimensional molecular variables for the same samples, have become increasingly available. To date, several comparison studies focused on feature selection methods for omics data, but to our knowledge, none compared these methods for the special case of multi-omics data. Given that these data have specific structures that differentiate them from single-omics data, it is unclear whether different feature selection strategies may be optimal for such data. In this paper, using 15 cancer multi-omics datasets we compared four filter methods, two embedded methods, and two wrapper methods with respect to their performance in the prediction of a binary outcome in several situations that

SUBMITTER: Li Y 

PROVIDER: S-EPMC9533501 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature