English(EN) HyCoSeq: Contextual Hyperbolic Representation Learning for Genomic Sequences

新的HyCoSeq框架使用双曲几何进行基因组序列学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了HyCoSeq，一个使用双曲几何学习基因组序列表示的新框架。该方法结合了加权洛伦兹残差聚合和双向长短期记忆网络，以捕捉DNA序列中的上下文关系。实验表明，即使没有广泛的预训练，HyCoSeq在与更大的预训练DNA语言模型相比时也表现出竞争力。 AI

影响这项研究通过提供更好的DNA序列理解模型，有可能提高基因组分析的效率和准确性。

排序理由该条目是一篇学术论文，详细介绍了一种在特定领域进行表示学习的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Chenhao Zeng, Zhibin Pu, Shufei Ge · 2026-09-16 04:00

HyCoSeq：用于基因组序列的上下文双曲表示学习

arXiv:2609.16925v1 Announce Type: cross 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 pat…