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Rethinking Performance Measures of RNA Secondary Structure Problems
- Publication Year :
- 2023
-
Abstract
- 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.<br />Comment: 12 pages, Accepted at the Machine Learning for Structural Biology Workshop, NeurIPS 2023
- Subjects :
- Quantitative Biology - Biomolecules
Computer Science - Machine Learning
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2401.05351
- Document Type :
- Working Paper