1756-Neural Network Architecture for Human Genome

The paper introduces the omnigenic neural network (OGM), a biologically structured architecture designed for human genome-scale phenotype prediction using the Gene Ontology (GO) as an inductive bias. By integrating individual-level genotypes, sex, and genetic principal components into a hierarchical gene and ontology layer, the model effectively captures non-additive interactions, such as epistasis, and accommodates multimodal data like metabolomics. Evaluated across large cohorts including the UK Biobank and the All of Us cohort, the OGM matches or outperforms traditional polygenic scores (PGS) and baseline models, especially in multilabel predictions and cross-ancestry transferability. Furthermore, the architecture provides systems-level interpretability and captures eQTL structures through methods like Integrated Gradients and Integrated Hessians, establishing a flexible framework for advanced genomic research.

References:

• Upmeier zu Belzen J, Arnoldt L, Hollmann N, et al. An interpretable omnigenic neural network architecture for the human genome[J]. medRxiv, 2026: 2026.07. 28.26359187.