Detection of deleterious variants by protein language models is improved by nucleotide conservation at three evolutionary timescales in rice
Abstract
This record contains the datasets, trained models, codes, and supporting materials for the manuscript “Detection of deleterious variants by protein language models is improved by nucleotide conservation at three evolutionary timescales in rice”. It includes standardized 'transformed' and 'non-transformed' training/validation/test datasets, prediction outputs for rice chromosome 12 (MAF validation), trained models, analysis scripts, and the conda environment file. See README.md for full details
Fields of Study & Subjects
Genomics and Rare Diseases
Genomics and Phylogenetic Studies
Machine Learning in Bioinformatics
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 8 January 2026
- Document type
- Dataset