Rethinking Performance Measures of RNA Secondary Structure Problems

Autor:	Runge, Frederic, Franke, Jörg K. H., Fertmann, Daniel, Hutter, Frank
Rok vydání:	2023
Předmět:	Quantitative Biology - Biomolecules Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 12 pages, Accepted at the Machine Learning for Structural Biology Workshop, NeurIPS 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.05351 Zobrazit plný text záznamu View this record from Arxiv