论文标题
RNA二级结构通过学习展开的算法来预测
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
论文作者
论文摘要
在本文中，我们提出了一个端到端的深度学习模型，称为E2FOLD，用于RNA二级结构预测，该预测可以有效地考虑到问题中的固有约束。 E2FOLD的关键思想是直接预测RNA碱基配对矩阵，并使用传开的算法将约束编程作为深度体系结构执行约束的模板。通过在基准数据集上进行全面的实验，我们证明了E2FOLD的出色性能：它与以前的SOTA相比（尤其是对于伪型结构）的结构明显更好，同时在推理时间上的效率与最快的算法一样有效。
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.
