# Rethinking Performance Measures of RNA Secondary Structure Problems

arXiv: https://arxiv.org/abs/2401.05351 · HTML: https://mlsanity.com/paper/2401.05351/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 50 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Learning to Design RNA](https://mlsanity.com/paper/1812.11951/) (2018)
- [Designing RNA Secondary Structures is Hard](https://mlsanity.com/paper/1710.11513/) (2017)
- [LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search](https://mlsanity.com/paper/2001.04020/) (2019)
- [A Novel Higher-order Weisfeiler-Lehman Graph Convolution](https://mlsanity.com/paper/2007.00346/) (2020)
- [Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design](https://mlsanity.com/paper/2205.13927/) (2022)
- [Scalable Deep Learning for RNA Secondary Structure Prediction](https://mlsanity.com/paper/2307.10073/) (2023)
- [Towards Automated Design of Riboswitches](https://mlsanity.com/paper/2307.08801/) (2023)

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

- [Comprehensive benchmarking of large language models for RNA secondary structure prediction](https://mlsanity.com/paper/2410.16212/) (2024)

## Abstract (arXiv)

Accurate RNA secondary structure prediction is vital for understanding cellular regulation and disease mechanisms. Deep learning (DL) methods have surpassed traditional algorithms by predicting complex features like pseudoknots and multi-interacting base pairs. However, traditional distance measures can hardly deal with such tertiary interactions and the currently used evaluation measures (F1 score, MCC) have limitations. We propose the Weisfeiler-Lehman graph kernel (WL) as an alternative metric. Embracing graph-based metrics like WL enables fair and accurate evaluation of RNA structure prediction algorithms. Further, WL provides informative guidance, as demonstrated in an RNA design experiment.