Accurate prediction of RNA secondary structure is crucial for understanding the intricate mechanisms involved in cellular regulation and disease processes. While traditional algorithms have been used in the past for this purpose, deep learning (DL) methods have taken the lead by successfully predicting complex features such as pseudoknots and multi-interacting base pairs.

However, one of the challenges faced in evaluating these DL methods lies in the difficulty of handling tertiary interactions in RNA structures. The traditional distance measures that are commonly used are not well-equipped to handle such interactions. Additionally, the evaluation measures currently used, such as F1 score and MCC (Matthews correlation coefficient), have their own limitations.

In this article, the Weisfeiler-Lehman graph kernel (WL) is proposed as an alternative metric for evaluating RNA structure prediction algorithms. By embracing graph-based metrics like WL, researchers can achieve fair and accurate evaluations. The use of WL as a metric not only provides a better evaluation framework for RNA structure prediction algorithms, but also offers valuable insights and guidance.

An RNA design experiment demonstrated the informative nature of WL as a guidance tool. With WL, researchers can gain a deeper understanding of the predicted RNA structures and make more informed decisions in the design process.

This article highlights the importance of accurate evaluation in RNA structure prediction and the role that graph-based metrics like WL can play in improving this evaluation process. By using WL as an alternative metric, researchers can achieve more comprehensive and insightful assessments of their prediction algorithms, ultimately leading to advancements in our understanding of cellular regulation and disease mechanisms.

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