  Statistical learning on biological data can be challenging due to confounding
variables in sample collection and processing. Confounders can cause models to
generalize poorly and result in inaccurate prediction performance metrics if
models are not validated thoroughly. In this paper, we propose methods to
control for confounding factors and further improve prediction performance. We
introduce OrthoNormal basis construction In cOnfounding factor Normalization
(ONION) to remove confounding covariates and use the Domain-Adversarial Neural
Network (DANN) to penalize models for encoding confounder information. We apply
the proposed methods to simulated and empirical patient data and show
significant improvements in generalization.
