🧬 New preprint! An interpretable omnigenic neural network architecture for the human genome We introduce the Omnigenic Model (OGM), a neural network architecture for human genetics. It uses biological system structure to aggregate genetic variation for disease risk prediction.

Current multimodal single-cell integration methods act as black boxes, lacking meaningful interpretability. We introduce NetworkVI, a VAE that performs integration via gene-gene interactions and the Gene Ontology for biologically grounded analysis. www.biorxiv.org/content/10.1...