Robustifying genomic classifiers to batch effects via ensemble learning.

ABSTRACT:

Motivation

Genomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across batches. Such 'batch effects' often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here, we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods.

Results

We provide a systematic comparison between these two strategies using studie

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8485848 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature