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HyCoSeq introduces a novel framework leveraging hyperbolic geometry and Lorentz convolutions to enhance genomic sequence representation learning without relying on massive pretraining datasets. By integrating weighted residual aggregation with bidirectional context modeling, the method achieves competitive performance against larger DNA language models while maintaining geometric consistency in local feature aggregation. This advancement highlights the potential of specialized mathematical inductive biases to improve efficiency and accuracy in bioinformatics applications.
HyCoSeq: Contextual Hyperbolic Representation Learning for Genomic Sequences
arXiv:2609.16925v1 Announce Type: new Abstract: Hyperbolic geometry provides a natural inductive bias for genomic representation learning, but existing hyperbolic genomic models primarily use Lorentz convolutions to learn local sequence representations, while their residual pathways do not directly aggregate full Lorentz representations. We propose HyCoSeq, a contextual hyperbolic representation learning framework for genomic sequences. HyCoSeq incorporates weighted Lorentzian residual aggregation into multi-curvature Lorentz encoding, allowing full Lorentz representations to participate directly in geometry-consistent local aggregation. It further introduces a bidirectional long short-term memory network that integrates information from both sequence directions to learn contextual relationships among local representations at different positions within a genomic sequence, thereby extending local hyperbolic convolutional encoding to sequence-level contextualized representations. Extensive experiments across diverse genomic tasks show that HyCoSeq outperforms existing hyperbolic baselines and, without large-scale genomic pretraining, achieves competitive performance against substantially larger pretrained DNA language models.
Abstract:Hyperbolic geometry provides a natural inductive bias for genomic representation learning, but existing hyperbolic genomic models primarily use Lorentz convolutions to learn local sequence representations, while their residual pathways do not directly aggregate full Lorentz representations. We propose HyCoSeq, a contextual hyperbolic representation learning framework for genomic sequences. HyCoSeq incorporates weighted Lorentzian residual aggregation into multi-curvature Lorentz encoding, allowing full Lorentz representations to participate directly in geometry-consistent local aggregation. It further introduces a bidirectional long short-term memory network that integrates information from both sequence directions to learn contextual relationships among local representations at different positions within a genomic sequence, thereby extending local hyperbolic convolutional encoding to sequence-level contextualized representations. Extensive experiments across diverse genomic tasks show that HyCoSeq outperforms existing hyperbolic baselines and, without large-scale genomic pretraining, achieves competitive performance against substantially larger pretrained DNA language models.
Machine Learning (cs.LG); Genomics (q-bio.GN); Machine Learning (stat.ML)
arXiv:2609.16925 [cs.LG]
(or arXiv:2609.16925v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.16925 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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