The Platonic gene: convergent representations across biological models learn cell plausibility

Protein coding genes can be represented in myriad ways, including their nucleic acid or protein sequences, molecular networks, textual annotations, co-expression in cells, and perturbation phenotypes. The Platonic representation hypothesis proposes that increasingly capable models converge toward shared representations of the underlying world, even when trained on different modalities. Here, we test this hypothesis for gene biology. We find that representations from different modalities, architectures, and training objectives can agree on the relationships between genes. This agreement is stronger among models that predict biological processes better, in accordance with the Platonic representation hypothesis. Nonetheless, convergence is not perfect, raising the question whether differences between representations capture complementary biological information that can be used to obtain a more holistic representation. We show that combining six models into a shared PlantaGenet consensus representation improves gene-property prediction. We use the geometry of genes in the PlantaGenet consensus space to assess the biological plausibility of single-cell expression profiles. In a combined human blood and brain evaluation, cell coherence and dominant-dimensionality scores distinguish real observed cells from gene-shuffled, independently sampled gene counts, and cross-tissue chimeric expression profiles, achieving AUROCs of 1.000, 0.992, and 0.845, respectively, outperforming individual models. These results suggest that biological models capture both shared and complementary gene relationships, and that integrating these relationships provides a geometric approach to assess gene-expression coherence, with potential implications for biological discovery and cell engineering.