Caduceus:双向等变远程DNA序列建模。

Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov
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引用次数: 0

摘要

大规模序列建模已经引发了快速发展,现在延伸到生物学和基因组学。然而,基因组序列建模带来了挑战,例如需要对远程标记相互作用、基因组上游和下游区域的影响以及DNA的反向互补(RC)进行建模。在这里,我们提出了一种基于这些挑战的架构,该架构建立在远程Mamba块的基础上,并将其扩展到支持双向性的BiMamba组件,以及额外支持RC等方差的MambaDNA块。我们使用MambaDNA作为第一族RC等变双向远程DNA语言模型Caduceus的基础,并引入预训练和微调策略,生成Caduceus DNA基础模型。Caduceus在下游基准上优于以前的远程模型;在具有挑战性的长期变异效应预测任务中,Caduceus的性能超过了不利用双向性或等方差的10倍较大模型。复制我们实验的代码在这里。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling.

Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics. However, modeling genomic sequences introduces challenges such as the need to model long-range token interactions, the effects of upstream and downstream regions of the genome, and the reverse complementarity (RC) of DNA Here, we propose an architecture motivated by these challenges that builds off the long-range Mamba block, and extends it to a BiMamba component that supports bi-directionality, and to a MambaDNA block that additionally supports RC equivariance. We use MambaDNA as the basis of Caduceus, the first family of RC equivariant bi-directional long-range DNA language models, and we introduce pre-training and fine-tuning strategies that yield Caduceus DNA foundation models. Caduceus outperforms previous long-range models on downstream benchmarks; on a challenging long-range variant effect prediction task, Caduceus exceeds the performance of 10 x larger models that do not leverage bi-directionality or equivariance. Code to reproduce our experiments is available here.

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