Impact of U2-type introns on splice site prediction in A. thaliana species using deep learning
The obtained results contribute to a deeper understanding of how intronic features influence splice site prediction and suggest that future prediction models should consider factors such as intron length, multiplicity, and the spatial arrangement of splice-related signals.
Authors
- Wesley De Neve (ORCID: https://orcid.org/0000-0002-8190-3839)
- Espoir Kabanga (ORCID: https://orcid.org/0000-0002-9523-826X)
- Arnout Van Messem (ORCID: https://orcid.org/0000-0001-8545-7437)
- Stephen Depuydt (ORCID: https://orcid.org/0000-0001-8965-1026)
- Soeun Yun
- Seonil Jee
Institutions
- HOGENT University of Applied Sciences and Arts (BE)
- University of Liège (BE)
- Ghent University Hospital (BE)
- Ghent University (BE)
- Ghent University Global Campus (KR)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1186/s12859-025-06315-w
- Primary Topic
- RNA Research and Splicing
- Type
- article
- Field-Weighted Citation Impact
- 0.00