Andrei Slabodkin

This paper introduces LIgO, a software suite that simulates adaptive immune receptor repertoires with immune information to aid in developing and benchmarking AIRR machine learning methods.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

The paper introduces manufacturing-aware generative models to enable efficient biological sequence design and synthesis at petascale, reducing costs by a trillion-fold.

The paper shows that simple computational methods like BLOSUM can create diverse, binder-enriched antibody libraries better than deep learning approaches.

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

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.