Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking study.

ABSTRACT:

Motivation

High-throughput technologies allow comprehensive characterization of individuals on many molecular levels. However, training computational models to predict disease status based on omics data is challenging. A promising solution is the integration of external knowledge about structural and functional relationships into the modeling process. We compared four published random forest-based approaches using two simulation studies and nine experimental datasets.

Results

The self-sufficient prediction error approach should be applied when large numbers of relevant pathways are expected. The competing methods hunting and learner of functional enrichment should be used when low numbers of relevant pathways are expected or the most strongly associated pathways are of inter

SUBMITTER: Seifert S 

PROVIDER: S-EPMC7520048 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature