Understanding the connection between complex structural features of RNA and biological function is a fundamental challenge in evolutionary studies and in RNA design. However, building datasets of RNA 3D structures and making appropriate modeling choices remains time-consuming and lacks standardization. In this chapter, we describe the use of rnaglib, to train supervised and unsupervised machine learning-based function prediction models on datasets of RNA 3D structures.
@article{arxiv.2402.09330,
title = {3D-based RNA function prediction tools in rnaglib},
author = {Carlos Oliver and Vincent Mallet and Jérôme Waldispühl},
journal= {arXiv preprint arXiv:2402.09330},
year = {2024}
}