Camous Moslemi

The paper validates variant effect predictions in a Brachypodium distachyon mutant population using burden and purging tests, showing ESM outperforms SIFT and PlantCAD for missense variants.

The paper develops deep learning models to predict gene expression across species and assesses their ability to capture regulatory variant effects in Brachypodium mutant lines, showing improved accuracy over existing methods.

The paper develops a machine learning method to predict blood group antigens and HPA-1 from genotyping data, achieving high accuracy in multiple cohorts.

The paper analyzes introgression and selection patterns in symbiosis genes within Rhizobium leguminosarum isolates, finding limited introgression confined to plasmids and chromosomal islands.

Syntenizer 3000 filters paralogs from gene groups using synteny analysis to identify true syntelogs and provides tools for visualization and classification.