Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction.

ABSTRACT: Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same tr

SUBMITTER: Qiu X 

PROVIDER: S-EPMC10138783 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature