# Deciphering RNA Secondary Structure Prediction: A Probabilistic K-Rook Matching Perspective

arXiv: https://arxiv.org/abs/2212.14041 · HTML: https://mlsanity.com/paper/2212.14041/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 69 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Transformer Quality in Linear Time](https://mlsanity.com/paper/2202.10447/) (2022)
- [Primer: Searching for Efficient Transformers for Language Modeling](https://mlsanity.com/paper/2109.08668/) (2021)
- [DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2211.11214/) (2022)
- [Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model](https://mlsanity.com/paper/1609.00680/) (2016)
- [RNA Secondary Structure Prediction By Learning Unrolled Algorithms](https://mlsanity.com/paper/2002.05810/) (2020)
- [LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search](https://mlsanity.com/paper/2001.04020/) (2019)
- [Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design](https://mlsanity.com/paper/2205.13927/) (2022)

## Built on by (most cited first, 1 of 1)

- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)

## Abstract (arXiv)

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although deep learning has shown promising results in this field, current methods suffer from poor generalization and high complexity. In this work, we reformulate the RNA secondary structure prediction as a K-Rook problem, thereby simplifying the prediction process into probabilistic matching within a finite solution space. Building on this innovative perspective, we introduce RFold, a simple yet effective method that learns to predict the most matching K-Rook solution from the given sequence. RFold employs a bi-dimensional optimization strategy that decomposes the probabilistic matching problem into row-wise and column-wise components to reduce the matching complexity, simplifying the solving process while guaranteeing the validity of the output. Extensive experiments demonstrate that RFold achieves competitive performance and about eight times faster inference efficiency than the state-of-the-art approaches. The code and Colab demo are available in (http://github.com/A4Bio/RFold).