G. K. Sandve

The paper develops AIRRTM, a generative model that identifies and generates disease-associated adaptive immune receptor sequences using only repertoire-level immune status data.

ANDA automates the analysis of neuronal morphology from live-cell imaging data, demonstrating accuracy and efficiency across rat, chicken, and human models.

This paper demonstrates that deep generative models can design high-affinity, developable antibodies at scale using synthetic data and transfer learning.

The paper introduces Absolut!, a software suite generating synthetic antibody-antigen structures to benchmark machine learning methods for antibody specificity prediction.

The paper shows that VDJ recombination rules vary between individuals, leading to individual-specific Ig sequence spaces.

The paper identifies a compact vocabulary of paratope-epitope interaction motifs that enables machine learning prediction of antibody-antigen binding.