Detection of deleterious variants by protein language models is improved by nucleotide conservation at three evolutionary timescales in rice

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Authors: Behrooz Vahedi, Camous Moslemi, T Asp, Xiaqing Yu, Guillaume P. Ramstein
Cited by: 0

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