PolyA-GLM: A comprehensive framework for De novo polyadenylation site prediction using genome language models
Published in Computational and Structural Biotechnology Journal (CSBJ), Elsevier, 2025
| First Author | CSBJ, Elsevier, Q1, Impact Factor 5.2 |
- Constructed a dataset (with hard negatives) to train language models on polyadenylation site detection.
- Showed that models can learn biologically validated features through different types of experiments.
- Conducted a genome-wide de novo polyadenylation site discovery.
Recommended citation: Saha, S. et al. (2025). "PolyA-GLM: A comprehensive framework for De novo polyadenylation site prediction using genome language models." Computational and Structural Biotechnology Journal. Elsevier, Q1, Impact Factor 5.2.