Predicting RNA sequence-structure likelihood via structure-aware deep learning
In this study, we propose two deep learning models, NU-ResNet and NUMO-ResNet, to evaluate RNA sequence-secondary structure pairs. These two models expand the field of data-driven approaches for learning RNA. Furthermore, these two models provide the new method to encode RNA sequence-secondary structure pairs.
Authors
- Giulia Pedrielli (ORCID: https://orcid.org/0000-0001-6726-9790)
- Teresa Wu (ORCID: https://orcid.org/0000-0002-0529-7048)
- You Zhou (ORCID: https://orcid.org/0009-0002-2095-3282)
- Fei Zhang (ORCID: https://orcid.org/0000-0002-3177-7547)
Institutions
- Rutgers, The State University of New Jersey (US)
- Arizona State University (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-09-30
- DOI
- https://doi.org/10.1186/s12859-024-05916-1
- Citations
- 3
- Primary Topic
- RNA and protein synthesis mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- National Science Foundation
- Arizona State University