Published March 6, 2026
| Version v1
Dataset
Open
Genomic language models improve cross-species gene expression prediction and accurately capture regulatory variant effects in Brachypodium mutant lines
Authors/Creators
-
1.
Aarhus University
- 2. Department of Agroecology, Aarhus University, Slagelse 4200, Denmark
- 3. State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, Department of Plant Genetics and Breeding, China Agricultural University, Beijing 100193, People's Republic of China
-
4.
Nanjing Agricultural University
Description
This record contains the datasets, trained models, codes, and supporting materials for the manuscript “Genomic language models improve cross-species gene expression prediction and accurately capture regulatory variant effects in Brachypodium mutant lines”. It contains datasets including sequence embeddings across 17 plant species used for training, hyperparameter optimization, cross-validation tests, and experimental in planta validation of predictions, gene expression predictions across 17 species by each of the 4 EMPRES model types, trained models, analysis scripts, and the conda environment file. See README.md for full details.
Files
codes.zip
Files
(129.4 GB)
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Additional details
Software
- Repository URL
- https://github.com/behroozvahedi/Regeffects
- Programming language
- Python
- Development Status
- Active