A Comprehensive Benchmark for RNA 3D Structure-Function Modeling
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908605126541312 |
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| author | Wyss, Luis Mallet, Vincent Karroucha, Wissam Borgwardt, Karsten Oliver, Carlos |
| author_facet | Wyss, Luis Mallet, Vincent Karroucha, Wissam Borgwardt, Karsten Oliver, Carlos |
| contents | The relationship between RNA structure and function has recently attracted interest within the deep learning community, a trend expected to intensify as nucleic acid structure models advance. Despite this momentum, the lack of standardized, accessible benchmarks for applying deep learning to RNA 3D structures hinders progress. To this end, we introduce a collection of seven benchmarking datasets specifically designed to support RNA structure-function prediction. Built on top of the established Python package rnaglib, our library streamlines data distribution and encoding, provides tools for dataset splitting and evaluation, and offers a comprehensive, user-friendly environment for model comparison. The modular and reproducible design of our datasets encourages community contributions and enables rapid customization. To demonstrate the utility of our benchmarks, we report baseline results for all tasks using a relational graph neural network. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Benchmark for RNA 3D Structure-Function Modeling Wyss, Luis Mallet, Vincent Karroucha, Wissam Borgwardt, Karsten Oliver, Carlos Biomolecules Machine Learning The relationship between RNA structure and function has recently attracted interest within the deep learning community, a trend expected to intensify as nucleic acid structure models advance. Despite this momentum, the lack of standardized, accessible benchmarks for applying deep learning to RNA 3D structures hinders progress. To this end, we introduce a collection of seven benchmarking datasets specifically designed to support RNA structure-function prediction. Built on top of the established Python package rnaglib, our library streamlines data distribution and encoding, provides tools for dataset splitting and evaluation, and offers a comprehensive, user-friendly environment for model comparison. The modular and reproducible design of our datasets encourages community contributions and enables rapid customization. To demonstrate the utility of our benchmarks, we report baseline results for all tasks using a relational graph neural network. |
| title | A Comprehensive Benchmark for RNA 3D Structure-Function Modeling |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2503.21681 |