Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants
Under a unified benchmark, CNN architectures are most reliable for esti-mating enhancer regulatory effects of SNPs, while hybrid CNN-Transformer models are superior for causal SNP identification within LD. These comparisons help guide model selection for variant-effect prediction in noncoding regions.
Authors
- Gaetano Manzo (ORCID: https://orcid.org/0000-0002-1682-6865)
- Ivan Ovcharenko (ORCID: https://orcid.org/0000-0002-9730-7732)
- Karol Borkowski (ORCID: https://orcid.org/0000-0003-2340-4844)
Institutions
- National Institutes of Health (US)
- Case Western Reserve University (US)
Publication Details
- Journal
- Genes
- Published
- 2025-10-15
- DOI
- https://doi.org/10.3390/genes16101223
- Citations
- 1
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Institutes of Health
- U.S. National Library of Medicine
- NHLBI Division of Intramural Research