Hyperbolic Genome Embeddings

ICLR 2025 Conference Submission13373 Authors

Keywords: genomics, representation learning, hyperbolic geometry

TL;DR: A hyperbolic geometry-based approach for genomic sequence representation learning

Abstract: Current approaches to genomic sequence modeling often struggle to align the inductive biases of machine learning models with the evolutionarily-informed structure of biological systems. To this end, we formulate a novel application of hyperbolic CNNs that exploits this structure, enabling more expressive DNA sequence representations. Our strategy circumvents the need for explicit phylogenetic mapping while discerning key properties of sequences pertaining to core functional and regulatory behavior. Across 37 out of 43 genome interpretation benchmark datasets, our hyperbolic models outperform their Euclidean equivalents. Notably, our approach even surpasses state-of-the-art performance on seven GUE benchmark datasets, consistently outperforming many DNA language models while using 13-379$\times$ fewer parameters and avoiding pretraining. Our results include a novel benchmark dataset - the Transposable Elements Benchmark - which explores a significant but understudied component of the genome with deep evolutionary significance. We further motivate our work by constructing an empirical method for interpreting the hyperbolicity of dataset embeddings. Throughout these assessments, we find persistent evidence highlighting the potential of our hyperbolic framework as a robust paradigm for genome representation learning.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

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Submission Number: 13373