Classification based on extensions of LS-PLS using logistic regression: application to clinical and multiple genomic data.

ABSTRACT:

Background

To address high-dimensional genomic data, most of the proposed prediction methods make use of genomic data alone without considering clinical data, which are often available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions. We consider here methods for classification purposes that simultaneously use both types of variables but apply dimensionality reduction only to the high-dimensional genomic ones.

Results

Using partial least squares (PLS), we propose some one-step approaches based on three extensions of the least squares (LS)-PLS method for logistic regression. A comparison of their prediction performances via a simulation and on real data sets from cancer studies is conducted.

SUBMITTER: Bazzoli C 

PROVIDER: S-EPMC6127926 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature