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.

我们提出了一种端到端的深度学习模型 E2Efold，用于 RNA 二级结构预测，可以有效地考虑问题中的固有约束，并通过约束规划的未卷积算法作为深度结构的模板强制实施约束，全面实验表明，E2Efold 具有优越的性能：相对于之前的 SOTA，特别是对于伪结节数量结构的预测具有显著的改进，同时推断时间也不会慢于最快的算法。