Sanne Abeln

DeepPheno links SNP genotypes to hyperspectral imaging in lettuce using deep learning, showing predictive performance for known and novel genotype-phenotype associations.

The paper uses protein foundation models to predict microbial root competence across bacteria, finding that these models outperform traditional methods when predicting traits in evolutionarily distant species.

The paper shows that protein agglomerates in E. coli regulate cytoplasmic rheology, with diffusion inversely correlating with amino acid metabolism protein abundance.

This study compares sequence- and structure-based antibody clustering methods on simulated repertoire data, finding structure-based methods identify more multiple-occupancy clusters but have length constraints.

This study introduces tumor break load (TBL) as a measure of genomic instability across cancers, showing its association with gene expression changes and clinical outcomes.

This study evaluates immunogen properties to improve antibody selection by analyzing structural and functional features and developing a tool for applying these insights.

This study predicts protein association with extracellular vesicles using sequence-based models and identifies palmitoylation as a key determinant.

The paper introduces an ensemble of deep learning models (PIPENNs) for predicting protein interface residues, showing improved performance over existing methods across different interaction types.

The paper introduces SeRenDIP-CE, a sequence-based method for predicting conformational epitopes, achieving better performance than existing predictors on an antibody-antigen structure.