RNA Secondary Structure Prediction By Learning Unrolled Algorithms

Abstract

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.

arXiv commentInternational Conference on Learning Representations 2020

References in corpus (4)

• Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model
• Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
• Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
• GLAD: Learning Sparse Graph Recovery

Cited by in corpus (6)

• Review of Machine-Learning Methods for RNA Secondary Structure Prediction
• Learning to Optimize: A Primer and A Benchmark
• Learning the random variables in Monte Carlo simulations with stochastic gradient descent: Machine learning for parametric PDEs and financial derivative pricing
• Training Stronger Baselines for Learning to Optimize
• Learned Interpretable Residual Extragradient ISTA for Sparse Coding
• Neural representation and generation for RNA secondary structures