RNA Secondary Structure Prediction By Learning Unrolled Algorithms

ICLR 2020 Xinshi ChenYu LiRamzan UmarovXin GaoLe Song

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... (read more)

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