Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.

ABSTRACT: Introduction: Prediction of RNA secondary structure from single sequences still needs substantial improvements. The application of machine learning (ML) to this problem has become increasingly popular. However, ML algorithms are prone to overfitting, limiting the ability to learn more about the inherent mechanisms governing RNA folding. It is natural to use high-capacity models when solving such a difficult task, but poor generalization is expected when too few examples are available. Methods: Here, we report the relation between capacity and performance on a fundamental related problem: determining whether two sequences are fully complementary. Our analysis focused on the impact of model architecture and capacity as well as dataset size and nature on classification accuracy.

SUBMITTER: Chasles S 

PROVIDER: S-EPMC10507318 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature