Biological Graph Priors Enable Representation Learning for Cellular Microscopy

Learning meaningful representations from cellular microscopy images remains a central challenge in computational biology and computer vision. Recent advances in self-supervised learning (SSL) have shown that scaling model and dataset size can improve the recovery of biological relationships such as gene–gene or compound–target associations. However, this progress has been driven primarily by scale rather than structure—requiring vast data and compute while offering no guarantee that the resulting embeddings capture underlying biology. We introduce NOVA, a structure-first framework for learning biologically aligned representations of cellular perturbations. NOVA integrates curated biological graphs directly into the training objective of a vector-quantised transformer, aligning embeddings of related genes and pathways through graph-based regularisation. By incorporating these relational priors during training, NOVA learns biologically faithful representations with orders of magnitude less data and compute than current state-of-the-art SSL models, while achieving superior performance on gene-gene retrieval benchmarks. This approach reframes representation learning for microscopy from a problem of scale to one of structure, offering a more systematic and efficient route toward biologically grounded embeddings.