Luca Giancardo

The paper introduces a context-aware framework integrating text and protein language models to predict multiple antibody properties without retraining, achieving high correlation across developability properties.

The paper presents a computational workflow for designing nanobody binders against a novel cancer target, achieving sub-nanomolar affinities without experimental structure or prior antibody data.

This paper introduces an unsupervised deep learning method to derive brain imaging phenotypes from MRI data, identifying new genetic loci associated with these endophenotypes.

The paper introduces iGWAS, a method using self-supervised deep learning to extract phenotypes from medical images for GWAS, finding new genetic loci related to retinal vessel development.

This paper introduces a method to identify multivariate brain network differences between groups using sparse connectivity analysis, applied to human and mouse connectomes.