English(EN) Scaling RNA Foundation Models through Iterative Refinement

Eco-RNA模型采用迭代优化提升RNA预测精度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员开发了Eco-RNA，一个拥有8000万参数的RNA基础模型，该模型利用迭代优化来调整推理深度。通过重复处理序列表示，该方法使模型能够获得更高的准确性，迭代次数在训练后确定。与参数量大得多的Evo 2 40B等模型相比，Eco-RNA在RNAGym适应性基准测试中表现更优，尽管其参数量少得多。该模型在分类非编码RNA家族、预测RNA二级结构和预测化学探测谱方面也显示出更强的能力。 AI

影响为RNA基础模型引入了一个新的扩展维度，有可能提高生物序列分析的效率和性能。

排序理由详细介绍新模型架构和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Pengan Chen, Liang Hong, Xinyuan Liu, Han Huang, Ziqian Lin, Jiayang Chen, Yimin Fan, Jiyue Jiang, Yu Li · 2026-10-12 04:00

通过迭代精炼扩展RNA基础模型

arXiv:2610.13106v1 Announce Type: new Abstract: Biological function in RNA is encoded across scales, from local sequence motifs to long-range structural dependencies. Conventional RNA foundation models couple parameter capacity and inference depth within a fixed architecture, pre…

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通过迭代精炼扩展RNA基础模型

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