3D-based RNA function prediction tools in rnaglib
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929334496788480 |
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| author | Oliver, Carlos Mallet, Vincent Waldispühl, Jérôme |
| author_facet | Oliver, Carlos Mallet, Vincent Waldispühl, Jérôme |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D-based RNA function prediction tools in rnaglib Oliver, Carlos Mallet, Vincent Waldispühl, Jérôme Biomolecules Machine Learning 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. |
| title | 3D-based RNA function prediction tools in rnaglib |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2402.09330 |