The effect of non-linear signal in classification problems using gene expression.

ABSTRACT: Those building predictive models from transcriptomic data are faced with two conflicting perspectives. The first, based on the inherent high dimensionality of biological systems, supposes that complex non-linear models such as neural networks will better match complex biological systems. The second, imagining that complex systems will still be well predicted by simple dividing lines prefers linear models that are easier to interpret. We compare multi-layer neural networks and logistic regression across multiple prediction tasks on GTEx and Recount3 datasets and find evidence in favor of both possibilities. We verified the presence of non-linear signal when predicting tissue and metadata sex labels from expression data by removing the predictive linear signal with Limma, and showed the remo

SUBMITTER: Heil BJ 

PROVIDER: S-EPMC10079219 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature