Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration
Compared to existing models, Deep-m5U showed approximately 5.23% and 3.73% higher accuracy on the training data and 3.95% and 3.26% higher accuracy on independent samples for the Full Transcript and Mature mRNA datasets, respectively. The reliability and effectiveness of Deep-m5U make it a valuable tool for scientists and a potential asset in pharmaceutical design and research.
Authors
- Sumaiya Noor (ORCID: https://orcid.org/0000-0001-9739-4592)
- Salman A. AlQahtani (ORCID: https://orcid.org/0000-0003-1233-1774)
- Afshan Naseem (ORCID: https://orcid.org/0000-0002-4766-8070)
- Nijad Ahmad
- Salman Khan (ORCID: https://orcid.org/0000-0002-2905-1755)
- Hamid Hussain Awan
- Wasiq Aslam
Institutions
- Universiti Malaysia Terengganu (MY)
- Purdue University West Lafayette (US)
- King Saud University (SA)
- Khurasan University (AF)
- Management Sciences (United States) (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1186/s12859-024-05978-1
- Citations
- 41
- Primary Topic
- RNA modifications and cancer
- Type
- article
- Field-Weighted Citation Impact
- 9.59
Funders
- King Saud University