Beyond the black box with biologically informed neural networks

Abstract

Machine learning models for multi-omics data often trade off predictive accuracy against biological interpretability. An emerging class of deep learning architectures structurally encode biological knowledge to improve both prediction and explainability. Opportunities and challenges remain for broader adoption. Biologically informed neural networks promise to lead to more explainable, data-driven discoveries in genomics, drug development and precision medicine. Selby et al. highlight emerging opportunities, as well as challenges that will need to be overcome to enable their wider adoption.